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000-N18 - IBM Information Management DB2 10 Technical Mastery Test v3 - Dump Information

Vendor : IBM
Exam Code : 000-N18
Exam Name : IBM Information Management DB2 10 Technical Mastery Test v3
Questions and Answers : 35 Q & A
Updated On : December 14, 2018
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000-N18 IBM Information Management DB2 10 Technical Mastery Test v3

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000-N18 exam Dumps Source : IBM Information Management DB2 10 Technical Mastery Test v3

Test Code : 000-N18
Test Name : IBM Information Management DB2 10 Technical Mastery Test v3
Vendor Name : IBM
Q&A : 35 Real Questions

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IBM IBM Information Management DB2

IBM Db2 Statistical features for Analytics | killexams.com Real Questions and Pass4sure dumps

unless currently, business analytics against massive records and the commercial enterprise information warehouse needed to come from sophisticated application applications. This become as a result of many statistical functions similar to medians and quartiles were now not available in primary SQL, forcing the programs to retrieve enormous outcomes units and perform aggregations and data in the neighborhood. nowadays, many database administration systems have integrated these functions into SQL. This contains IBM's flagship product, Db2.

primary Analytics

Many enormous IT retail outlets applied massive records solutions over a decade in the past. at that time, the science of data was smartly-centered. business analysts already had loads of event analyzing subsets of records from operational programs in addition to time series statistics in the enterprise facts warehouse. These analyses covered simple statistical services akin to minima, maxima and capacity, in addition to superior features reminiscent of percentiles, cubes and rollups.

together with large data solutions came software packages that allowed company analysts to make use of a visible interface to select information points and specify aggregation criteria and statistical calculations. The utility then automatically creates SQL to gather the crucial information. besides the fact that children, a crucial efficiency difficulty arose with large facts. while scanning colossal quantities of data at once was a characteristic of massive facts solutions, advanced statistical calculations could not be accomplished there.

This required returning big amounts of information to the analyst’s utility, which required a configuration with large amounts of memory and CPU vigour. This also spawned the concept of developing native facts marts to cling subsets of the warehouse and big statistics with the intention to run wide statistical calculations in the neighborhood. happily, database administration techniques (DBMSs) stepped up and delivered multiple new SQL functions to support business analysts.

Most DBMSs already provided simple statistical operations such because the following:

  • Sum, minimal and highest
  • common (arithmetic mean)
  • standard deviation
  • Variance and covariance
  • Correlation
  • In contemporary DBMSs that help a big statistics answer (and, to a lesser extent an business statistics warehouse), it's now essential to support more advanced features for usability and efficiency explanations.

    IBM Db2 SQL Enhancements

    IBM has implemented multiple statistical capabilities in its flagship relational database product Db2. These encompass Median and Percentiles as well as dice and Rollup.

    Median

    Calculating an ordinary of a group of numbers appears like a simple operation. however, the time period “common” has diverse meanings in information. Three of those are the suggest, the median and the mode. Even the imply has a couple of variations. The arithmetic mean is probably the most prevalent imply, and this characteristic has existed in ANSI normal SQL for a long time. In contemporary version of Db2, IBM has expanded its SQL variant to encompass a median characteristic.

    Percentiles

    The percentile is an mixture feature that returns the facts cost inside a gaggle of values that corresponds to a given percentile. For readability, the median price of a group of numbers is the cost this is at the fiftieth percentile. If the number of values in the community is even, then the median is interpolated as being between both nearest values. for example, in the set (1, 2, 3), the number 2 is the median, or fiftieth percentile. within the set (1,2,three,4) the median is calculated as 2.5. Percentiles are a common approach of depicting information graphically, probably the most normal illustration being pie charts.

    The percentile function continues information aggregation, sorting and calculation operations on the host computing device, warding off downloads of massive outcome units for native calculation. It additionally simplifies SQL statements, allowing the database administrator (DBA) the probability to capture and tune analytical queries with the purpose of expanding efficiency and reducing useful resource utilization. as an instance, if medians and percentiles are required for a particular column price, the DBA may accept as true with an index on that column; however, there are a considerable number of methods (discussed under) to “pre-mixture” normal calculations.

    cube and Rollup

    The dice and rollup features are identical, in that they are a method for analyzing subtotals of a gaggle of facts objects, with rollup being a subset of dice. as an instance, trust an enterprise accounting system with distinct debts owned with the aid of each and every branch. to analyze account balances across the company an analyst needs to calculate subtotals for every department, as well as an usual total. during this state of affairs, the debts roll as much as departments, which then roll as much as a grand total. This may be coded in SQL during this method:

    select  Department_ID, SUM (Account_Balance)

    FROM  Account_Table

    group by way of ROLLUP (Department_ID)

    ORDER by using  Department_ID

    This SQL commentary generates a result set with an account steadiness subtotal row for every branch adopted via a final grand total row. more complicated statements are feasible, including distinctive tiers of subtotals and specification of distinctive grouping qualities. As with medians and percentiles, including the rollup definition within the SQL allows for the DBMS to do a single move of the records and function the required calculations effectively.

    The cube characteristic works in an identical fashion by means of allowing specification of grouping criteria. (See hyperlink on the end of this text for particulars.)  agree with here Account table:

    Account_Table

    Account_IDBalanceCustomer_IDCustomer_TypeAccount_TypeCustomer_Region...

    The remaining three columns are candidates for grouping, as a enterprise analyst may need to assessment a summary of debts for selected client types or account forms, or they might need to examine shoppers throughout distinctive areas. when it comes to the rollup function, the need can be to create subtotals for each and every of those columns, or for combinations of those columns. Some possible necessities should be would becould very well be:

  • regular balance for each and every client category
  • minimal and maximum balance for each combination of client classification and account classification
  • regular balance in each and every location with subtotals for each customer type
  • as opposed to code dissimilar SQL statements for each and every possible rollup, the cube function can be used to accomplish this in a single remark:

    choose  Customer_Type, Account_Type, Customer_Region, SUM(Account_Balance)

    FROM Account_Table

    neighborhood with the aid of dice  (Customer_Type, Account_Type, Customer_Region)

    ORDER with the aid of  (Customer_Type, Account_Type, Customer_Region)

    The outcome back by way of this statement is a outcome set of rows with right here:

  • Subtotal for each and every aggregate of (Customer_Type, Account_Type, Customer_Region)
  • Subtotal for each aggregate of (Customer_Type, Account_Type)
  • Subtotal for every combination of (Customer_Type, Customer_Region)
  • Subtotal for each and every combination of (Account_Type, Customer_Region)
  • Subtotal for each and every aggregate of (Customer_Type)
  • Subtotal for every aggregate of (Account_Type)
  • Subtotal for each combination of (Customer_Region)
  • Grand complete
  • The means of this extraordinarily basic SQL observation to bring numerous rollups is a great boon to each the enterprise analyst and the DBA. Simplified SQL capacity fewer mistakes, easier debugging and stronger recognition of tuning needs.

    Tuning for Analytics

    The DBA who helps company analysts has a couple of alternatives for decreasing equipment supplies whereas offering quickly question response times. One components is to enforce a large records answer such because the IBM Db2 Analytics Accelerator (IDAA), a hybrid of application and hardware that combines an immense disk storage array with hugely parallel processing. Allocating Db2 tables within the IDAA permits the Db2 Optimizer to direct SQL statements in opposition t these tables to the IDAA, and this constantly capacity extremely quickly query execution times. a different option is to shop tables in both native Db2 and within the IDAA. The talents of this alternative is to deliver varied access paths to a particular table, considering native Db2 tables can have indexes defined on their columns.

    a third choice is to create abstract tables, on occasion referred to as materialized query tables (MQTs). The DBA creates these tables by means of defining an SQL statement it is used to populate the table, and then defining the times when the SQL statement is to be achieved. An illustration will aid clarify this.

    accept as true with our Account desk defined earlier:

    Account_Table

     

    Account_ID

    steadiness

    Customer_ID

    Customer_Type

    Account_Type

    Customer_Region

    ...

    count on that Account_Table exists in a data warehouse. This potential that it isn't a part of an operational device with ongoing on line activity and batch methods; rather, it contains rows which are loaded as soon as per day and stay static all the way through the day. Let’s additionally assume that the DBA has captured and analyzed standard SQL statements submitted through business analysts and decided that many queries require subtotals by way of Customer_Region.  it really is, many queries include right here SQL syntax:

    opt for  SUM (Account_Balance), ...

    FROM  Account_Table

    community by using ROLLUP (Department_ID)

    ...

    For this static desk, the rollup is calculated each time one of those queries executes, with identical effects. The DBA can lower aid usage by means of making a materialized question table like this one:

     

    CREATE table  Rollup_Acct_Dept_ID

       AS (opt for  SUM (Account_Balance), ...

         FROM  Account_Table

               group through ROLLUP (Department_ID)

               ...

    After creating this desk, the DBA concerns the REFRESH command to populate it. table Rollup_Act_Dept_ID now carries rows with the subtotal guidance from Account_Table. when you consider that the table statistics is static in this instance, the rollup records need most effective be calculated as soon as per day. Queries that want the rollup information can now query the MQT without delay; alternatively, they could stay coded as-is, and the Db2 Optimizer will immediately entry the MQT rather than re-calculate the subtotals.

    abstract

    in the early days of big facts, enterprise analysts grew accustomed to getting fast effects from their queries. however, tables grew, each in variety of columns and in the number of present rows, and queries became more complicated and required lager quantities of information. finally, SQL analytical query performance grew to be an argument.

    one of the most challenging performance considerations was the boost in complexity of the statistical processes and methods performed on the data. facts-intensive capabilities that required multiple aggregations and subtotals or other services that were not supported directly in SQL needed to be carried out with the aid of the analyst’s application. This led to gathering giant effect sets and transporting them from the huge statistics application across the network to enable the BI application package to comprehensive the calculations.

    IBM’s Db2 now includes SQL alternate options that may operate such distinctive statistical services equivalent to percentiles and cubes in the DBMS. This no longer most effective tremendously reduces the amount of facts traversing the network, it additionally offers the DBA opportunities to tune total units of tables and functions in place of one SQL observation at a time.

     # # #

     For more assistance:


    IBM updates InfoSphere and DB2 at suggestions on Demand | killexams.com Real Questions and Pass4sure dumps

    IBM unveiled a new version of its flagship records integration product -- IBM InfoSphere counsel Server 8.5 -- at its guidance on Demand convention final week in Las Vegas. massive Blue additionally took the wraps off the newest edition of its mainstay database management device, IBM DB2.

    SearchDataManagement.com become at the conference and sat down with Bernie Spang, IBM’s director of counsel administration product approach, to get extra details about the new releases. Spang talked concerning the heritage of InfoSphere advice Server and DB2’s new capabilities, and he defined one of the reasons why IBM is so drawn to acquiring statistics warehouse appliance vendor Netezza. listed below are some excerpts from that conversation:

    could you supply me a quick background lesson on the IBM InfoSphere product line?

    Bernie Spang: It truly has multifaceted origins. The information stage and pleasant stage, cleaning and ETL capabilities come from the Ascential acquisition a few years ago. The federation and replication capabilities that are part of InfoSphere tips Server have a heritage returned in IBM below diverse names at diverse instances.

    What are some of the new capabilities in InfoSphere tips Server 8.5?

    Spang: probably the most pleasing issues about the InfoSphere guidance Server is the tool set that comes together with it for accelerating the creation of integration jobs, as well as new speedy-track capabilities and new business thesaurus capabilities [that] permit the collaboration between company and IT on what the meaning of statistics is and the way it flows collectively.

    what is the new InfoSphere Blueprint Director?

    Spang: That offers clients the ability to capture the top-quality practices for designing and constructing and laying out an integration job to make certain that you’re truly in response to business needs and you’re pulling the right information together until they’re within the approach. It’s one more layer of collaboration that we’ve built into the product, and it makes it possible for users to peer the best metrics linked to every piece of statistics as it strikes in the course of the method.

    What does Blueprint Director seem like to the conclusion consumer?

    Spang: It’s a visual environment the place you’re laying out the mixing and you’re defining it and then you could use the quick-track potential to generate the ETL jobs. It’s that visual toolset for outlining your integration mission. And it ties with the business word list, where the business users and IT are agreeing on the definition of phrases.

    What elements have you delivered within the new version of DB2?

    Spang: IBM DB2 edition 10 is a brand new product that we’re delivering this week. [It offers] out-of-the-box performance improvements as much as 40% for some workloads [and] stronger scalability. The different exciting element is a brand new ability that we’re calling DB2 time shuttle query – the capacity to query advice within the present, in the past and sooner or later. in case you’ve loaded counsel, like new pricing tips for subsequent quarter, which you could do queries as if it have been subsequent quarter. in case you have enterprise agreements or guidelines which are over a time period, which you can do queries in the future and base it on how the policies may be in impact at the moment. companies already try this today, but largely with the aid of writing utility code. by pushing it down into the database application, we’re drastically simplifying the manner and enormously decreasing the quantity of code.

    IBM is in the procedure of acquiring Westboro, Mass.-based mostly records warehouse equipment seller Netezza and its field programmable gate array processor technology. What exactly is the cost of this expertise?

    Spang: Processing velocity is accomplishing the laws of physics [in terms of its] ability to proceed to grow, while on the same time the deserve to system greater guidance and do more transactions is becoming unabated. So how do you get these subsequent-era performance improvements? you put the pieces together and tremendously optimize them for particular workloads. That capacity you must have the utility optimized for the hardware even down to the processor stage. The container programmable gate array means that you can definitely program at a chip level, [and that leads to] a whole lot more desirable speeds than having it written in application running on a widespread-purpose processor.


    IBM application Powers information administration equipment for Northeast Utilities | killexams.com Real Questions and Pass4sure dumps

    source: IBM

    October 17, 2007 15:15 ET

    built-in solution From IBM and Lighthouse Meets Regulatory Compliance Challenges

    LAS VEGAS, NV--(Marketwire - October 17, 2007) - IBM assistance on Demand conference -- Northeast Utilities (NU), New England's greatest utility gadget, has chosen an integrated records management answer from IBM (NYSE: IBM) and Lighthouse computing device capabilities, Inc., to satisfy its turning out to be number of facts administration, e mail archiving and compliance necessities.

    The integrated information management gadget will help NU reply to litigation and e-discovery regulatory compliance requirements through more advantageous managing, securing, storing and archiving e mail messages and electronic information.

    "Northeast Utilities looks to continue the momentum relocating forward as our new information counsel management software evolves into a strong and a success application. The synergies constructed with our IBM enterprise accomplice Lighthouse laptop features, and our technically proficient in-condominium team, have enabled us to efficiently installation and configure IBM's RM software tools. we're laying down a powerful foundation to accomplish our strategic commercial enterprise desires," referred to Greg Yatrousis, Northeast Utilities' IT Product supervisor.

    The newly carried out records administration device is anticipated to lower NU's working charges by way of cutting back the time and energy necessary to retrieve assistance. The system also will assist NU's statistics and assistance management policies by using deciding on the type and layout of corporate information, monitoring compliance with company and legal retention requirements for facts, deciding upon the custodians of record classes, and enforcing dependent security requirements and person access in response to felony and enterprise necessities.

    The IBM application enabling NU to make use of information as a strategic asset comprises: IBM DB2 content supervisor, IBM DB2 facts manager, IBM DB2 doc manager, IBM WebSphere suggestions Integration, IBM CommonStore, IBM DB2 content supervisor statistics Enabler, IBM content supervisor On Demand.

    About Northeast Utilities

    Northeast Utilities operates New England's biggest utility gadget serving greater than two million electric and herbal gasoline valued clientele in Connecticut, western Massachusetts and New Hampshire. NU has made a strategic resolution to focal point on regulated business opportunities. For greater assistance talk over with www.nu.com

    About Lighthouse computer capabilities

    Lighthouse computer functions is a trusted IT guide to main corporations throughout the northeast. Lighthouse is an IBM Premier company partner, and placed quantity 228 in VARBusiness 2007 ranking of the right 500 IT answer issuer corporations within the u . s . a .. Lighthouse is additionally winner of IBM's 2006 Beacon Award for universal Technical Excellence in a business accomplice. For extra advice visit www.LighthouseCS.com.

    For more counsel on IBM's enterprise content material management offerings, seek advice from http://www-306.ibm.com/application/information/cm/


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    Guide to vendor-specific IT security certifications | killexams.com real questions and Pass4sure dumps

    Despite the wide selection of vendor-specific information technology security certifications, identifying which...

    ones best suit your educational or career needs is fairly straightforward.

    This guide to vendor-specific IT security certifications includes an alphabetized table of security certification programs from various vendors, a brief description of each certification and advice for further details.

    Introduction: Choosing vendor-specific information technology security certifications

    The process of choosing the right vendor-specific information technology security certifications is much simpler than choosing vendor-neutral ones. In the vendor-neutral landscape, you must evaluate the pros and cons of various programs to select the best option. On the vendor-specific side, it's only necessary to follow these three steps:

  • Inventory your organization's security infrastructure and identify which vendors' products or services are present.
  • Check this guide (or vendor websites, for products not covered here) to determine whether a certification applies to the products or services in your organization.
  • Decide if spending the time and money to obtain such credentials (or to fund them for your employees) is worth the resulting benefits.
  • In an environment where qualified IT security professionals can choose from numerous job openings, the benefits of individual training and certifications can be hard to appraise.

    Many employers pay certification costs to develop and retain their employees, as well as to boost the organization's in-house expertise. Most see this as a win-win for employers and employees alike, though employers often require full or partial reimbursement for the related costs incurred if employees leave their jobs sooner than some specified payback period after certification.

    There have been quite a few changes since the last survey update in 2015. The Basic category saw a substantial jump in the number of available IT security certifications due to the addition of several Brainbench certifications, in addition to the Cisco Certified Network Associate (CCNA) Cyber Ops certification, the Fortinet Network Security Expert Program and new IBM certifications. 

    2017 IT security certification changes

    Certifications from AccessData, Check Point, IBM and Oracle were added to the Intermediate category, increasing the total number of certifications in that category, as well. However, the number of certifications in the Advanced category decreased, due to several IBM certifications being retired. 

    Vendor IT security certifications Basic information technology security certifications 

    Brainbench basic security certificationsBrainbench offers several basic-level information technology security certifications, each requiring the candidate to pass one exam. Brainbench security-related certifications include:

  • Backup Exec 11d (Symantec)
  • Check Point FireWall-1 Administration
  • Check Point Firewall-1 NG Administration
  • Cisco Security
  • Microsoft Security
  • NetBackup 6.5 (Symantec)
  • Source: Brainbench Information Security Administrator certifications

    CCNA Cyber OpsPrerequisites: None required; training is recommended.

    This associate-level certification prepares cybersecurity professionals for work as cybersecurity analysts responding to security incidents as part of a security operations center team in a large organization.

    The CCNA Cyber Ops certification requires candidates to pass two written exams.

    Source: Cisco Systems CCNA Cyber Ops

    CCNA SecurityPrerequisites: A valid Cisco CCNA Routing and Switching, Cisco Certified Entry Networking Technician or Cisco Certified Internetwork Expert (CCIE) certification.

    This credential validates that associate-level professionals are able to install, troubleshoot and monitor Cisco-routed and switched network devices for the purpose of protecting both the devices and networked data.

    A person with a CCNA Security certification can be expected to understand core security concepts, endpoint security, web and email content security, the management of secure access, and more. He should also be able to demonstrate skills for building a security infrastructure, identifying threats and vulnerabilities to networks, and mitigating security threats. CCNA credential holders also possess the technical skills and expertise necessary to manage protection mechanisms such as firewalls and intrusion prevention systems, network access, endpoint security solutions, and web and email security.

    The successful completion of one exam is required to obtain this credential.

    Source: Cisco Systems CCNA Security

    Check Point Certified Security Administrator (CCSA) R80Prerequisites: Basic knowledge of networking; CCSA training and six months to one year of experience with Check Point products are recommended.

    Check Point's foundation-level credential prepares individuals to install, configure and manage Check Point security system products and technologies, such as security gateways, firewalls and virtual private networks (VPNs). Credential holders also possess the skills necessary to secure network and internet communications, upgrade products, troubleshoot network connections, configure security policies, protect email and message content, defend networks from intrusions and other threats, analyze attacks, manage user access in a corporate LAN environment, and configure tunnels for remote access to corporate resources.

    Candidates must pass a single exam to obtain this credential.

    Source: Check Point CCSA Certification

    IBM Certified Associate -- Endpoint Manager V9.0Prerequisites: IBM suggests that candidates be highly familiar with the IBM Endpoint Manager V9.0 console. They should have experience taking actions; activating analyses; and using Fixlets, tasks and baselines in the environment. They should also understand patching, component services, client log files and troubleshooting within IBM Endpoint Manager.

    This credential recognizes professionals who use IBM Endpoint Manager V9.0 daily. Candidates for this certification should know the key concepts of Endpoint Manager, be able to describe the system's components and be able to use the console to perform routine tasks.

    Successful completion of one exam is required.

    Editor's note: IBM is retiring this certification as of May 31, 2017; there will be a follow-on test available as of April 2017 for IBM BigFix Compliance V9.5 Fundamental Administration, Test C2150-627.

    Source: IBM Certified Associate -- Endpoint Manager V9.0

    IBM Certified Associate -- Security Trusteer Fraud ProtectionPrerequisites: IBM recommends that candidates have experience with network data communications, network security, and the Windows and Mac operating systems.

    This credential pertains mainly to sales engineers who support the Trusteer Fraud product portfolio for web fraud management, and who can implement a Trusteer Fraud solution. Candidates must understand Trusteer product functionality, know how to deploy the product, and be able to troubleshoot the product and analyze the results.

    To obtain this certification, candidates must pass one exam.

    Source: IBM Certified Associate -- Security Trusteer Fraud Protection

    McAfee Product SpecialistPrerequisites: None required; completion of an associated training course is highly recommended.

    McAfee information technology security certification holders possess the knowledge and technical skills necessary to install, configure, manage and troubleshoot specific McAfee products, or, in some cases, a suite of products.

    Candidates should possess one to three years of direct experience with one of the specific product areas.

    The current products targeted by this credential include:

  • McAfee Advanced Threat Defense products
  • McAfee ePolicy Orchestrator and VirusScan products
  • McAfee Network Security Platform
  • McAfee Host Intrusion Prevention
  • McAfee Data Loss Prevention Endpoint products
  • McAfee Security Information and Event Management products
  • All credentials require passing one exam.

    Source: McAfee Certification Program

    Microsoft Technology Associate (MTA)Prerequisites: None; training recommended.

    This credential started as an academic-only credential for students, but Microsoft made it available to the general public in 2012.

    There are 10 different MTA credentials across three tracks (IT Infrastructure with five certs, Database with one and Development with four). The IT Infrastructure track includes a Security Fundamentals credential, and some of the other credentials include security components or topic areas.

    To earn each MTA certification, candidates must pass the corresponding exam. 

    Source: Microsoft MTA Certifications

    Fortinet Network Security Expert (NSE)Prerequisites: Vary by credential.

    The Fortinet NSE program has eight levels, each of which corresponds to a separate network security credential within the program. The credentials are:

  • NSE 1 -- Understand network security concepts.
  • NSE 2 -- Sell Fortinet gateway solutions.
  • NSE 3 (Associate) -- Sell Fortinet advanced security solutions.
  • NSE 4 (Professional) -- Configure and maintain FortiGate Unified Threat Management products.
  • NSE 5 (Analyst) -- Implement network security management and analytics.
  • NSE 6 (Specialist) – Understand advanced security technologies beyond the firewall.
  • NSE 7 (Troubleshooter) -- Troubleshoot internet security issues.
  • NSE 8 (Expert) -- Design, configure, install and troubleshoot a network security solution in a live environment.
  • NSE 1 is open to anyone, but is not required. The NSE 2 and NSE 3 information technology security certifications are available only to Fortinet employees and partners. Candidates for NSE 4 through NSE 8 should take the exams through Pearson VUE.

    Source: Fortinet NSE

    Symantec Certified Specialist (SCS)This security certification program focuses on data protection, high availability and security skills involving Symantec products.

    To become an SCS, candidates must select an area of focus and pass an exam. All the exams cover core elements, such as installation, configuration, product administration, day-to-day operation and troubleshooting for the selected focus area.

    As of this writing, the following exams are available:

  • Exam 250-215: Administration of Symantec Messaging Gateway 10.5
  • Exam 250-410: Administration of Symantec Control Compliance Suite 11.x
  • Exam 250-420: Administration of Symantec VIP
  • Exam 250-423: Administration of Symantec IT Management Suite 8.0
  • Exam 250-424: Administration of Data Loss Prevention 14.5
  • Exam 250-425: Administration of Symantec Cyber Security Services
  • Exam 250-426: Administration of Symantec Data Center Security -- Server Advanced 6.7
  • Exam 250-427: Administration of Symantec Advanced Threat Protection 2.0.2
  • Exam 250-428: Administration of Symantec Endpoint Protection 14
  • Exam 250-513: Administration of Symantec Data Loss Prevention 12
  • Source: Symantec Certification

    Intermediate information technology security certifications 

    AccessData Certified Examiner (ACE)Prerequisites: None required; the AccessData BootCamp and Advanced Forensic Toolkit (FTK) courses are recommended.

    This credential recognizes a professional's proficiency using AccessData's FTK, FTK Imager, Registry Viewer and Password Recovery Toolkit. However, candidates for the certification must also have moderate digital forensic knowledge and be able to interpret results gathered from AccessData tools.

    To obtain this certification, candidates must pass one online exam (which is free). Although a boot camp and advanced courses are available for a fee, AccessData provides a set of free exam preparation videos to help candidates who prefer to self-study.

    The certification is valid for two years, after which credential holders must take the current exam to maintain their certification.

    Source: Syntricate ACE Training

    Cisco Certified Network Professional (CCNP) Security Prerequisites: CCNA Security or any CCIE certification.

    This Cisco credential recognizes professionals who are responsible for router, switch, networking device and appliance security. Candidates must also know how to select, deploy, support and troubleshoot firewalls, VPNs and intrusion detection system/intrusion prevention system products in a networking environment.

    Successful completion of four exams is required.

    Source: Cisco Systems CCNP Security

    Check Point Certified Security Expert (CCSE)Prerequisite: CCSA certification R70 or later.

    This is an intermediate-level credential for security professionals seeking to demonstrate skills at maximizing the performance of security networks.

    A CCSE demonstrates a knowledge of strategies and advanced troubleshooting for Check Point's GAiA operating system, including installing and managing VPN implementations, advanced user management and firewall concepts, policies, and backing up and migrating security gateway and management servers, among other tasks. The CCSE focuses on Check Point's VPN, Security Gateway and Management Server systems.

    To acquire this credential, candidates must pass one exam.

    Source: Check Point CCSE program

    Cisco Cybersecurity SpecialistPrerequisites: None required; CCNA Security certification and an understanding of TCP/IP are strongly recommended.

    This Cisco credential targets IT security professionals who possess in-depth technical skills and knowledge in the field of threat detection and mitigation. The certification focuses on areas such as event monitoring, event analysis (traffic, alarm, security events) and incident response.

    One exam is required.

    Source: Cisco Systems Cybersecurity Specialist

    Certified SonicWall Security Administrator (CSSA)Prerequisites: None required; training is recommended.

    The CSSA exam covers basic administration of SonicWall appliances and the network and system security behind such appliances.

    Classroom training is available, but not required to earn the CSSA. Candidates must pass one exam to become certified.

    Source: SonicWall Certification programs

    EnCase Certified Examiner (EnCE)Prerequisites: Candidates must attend 64 hours of authorized training or have 12 months of computer forensic work experience. Completion of a formal application process is also required.

    Aimed at both private- and public-sector computer forensic specialists, this certification permits individuals to become certified in the use of Guidance Software's EnCase computer forensics tools and software.

    Individuals can gain this certification by passing a two-phase exam: a computer-based component and a practical component.

    Source: Guidance Software EnCE

    EnCase Certified eDiscovery Practitioner (EnCEP)Prerequisites: Candidates must attend one of two authorized training courses and have three months of experience in eDiscovery collection, processing and project management. A formal application process is also required.

    Aimed at both private- and public-sector computer forensic specialists, this certification permits individuals to become certified in the use of Guidance Software's EnCase eDiscovery software, and it recognizes their proficiency in eDiscovery planning, project management and best practices, from legal hold to file creation.

    EnCEP-certified professionals possess the technical skills necessary to manage e-discovery, including the search, collection, preservation and processing of electronically stored information in accordance with the Federal Rules of Civil Procedure.

    Individuals can gain this certification by passing a two-phase exam: a computer-based component and a scenario component.

    Source: Guidance Software EnCEP Certification Program

    IBM Certified Administrator -- Security Guardium V10.0Prerequisites: IBM recommends basic knowledge of operating systems and databases, hardware or virtual machines, networking and protocols, auditing and compliance, and information security guidelines.

    IBM Security Guardium is a suite of protection and monitoring tools designed to protect databases and big data sets. The IBM Certified Administrator -- Security Guardium credential is aimed at administrators who plan, install, configure and manage Guardium implementations. This may include monitoring the environment, including data; defining policy rules; and generating reports.

    Successful completion of one exam is required.

    Source: IBM Security Guardium Certification

    IBM Certified Administrator -- Security QRadar Risk Manager V7.2.6Prerequisites: IBM recommends a working knowledge of IBM Security QRadar SIEM Administration and IBM Security QRadar Risk Manager, as well as general knowledge of networking, risk management, system administration and network topology.

    QRadar Risk Manager automates the risk management process in enterprises by monitoring network device configurations and compliance. The IBM Certified Administrator -- Security QRadar Risk Manager V7.2.6 credential certifies administrators who use QRadar to manage security risks in their organization. Certification candidates must know how to review device configurations, manage devices, monitor policies, schedule tasks and generate reports.

    Successful completion of one exam is required.

    Source: IBM Security QRadar Risk Manager Certification

    IBM Certified Analyst -- Security SiteProtector System V3.1.1Prerequisites: IBM recommends a basic knowledge of the IBM Security Network Intrusion Prevention System (GX) V4.6.2, IBM Security Network Protection (XGS) V5.3.1, Microsoft SQL Server, Windows Server operating system administration and network security.

    The Security SiteProtector System enables organizations to centrally manage their network, server and endpoint security agents and appliances. The IBM Certified Analyst -- Security SiteProtector System V3.1.1 credential is designed to certify security analysts who use the SiteProtector System to monitor and manage events, monitor system health, optimize SiteProtector and generate reports.

    To obtain this certification, candidates must pass one exam.

    Source: IBM Security SiteProtector Certification

    Oracle Certified Expert, Oracle Solaris 10 Certified Security AdministratorPrerequisite: Oracle Certified Professional, Oracle Solaris 10 System Administrator.

    This credential aims to certify experienced Solaris 10 administrators with security interest and experience. It's a midrange credential that focuses on general security principles and features, installing systems securely, application and network security, principle of least privilege, cryptographic features, auditing, and zone security.

    A single exam -- geared toward the Solaris 10 operating system or the OpenSolaris environment -- is required to obtain this credential.

    Source: Oracle Solaris Certification

    Oracle Mobile SecurityPrerequisites: Oracle recommends that candidates understand enterprise mobility, mobile application management and mobile device management; have two years of experience implementing Oracle Access Management Suite Plus 11g; and have experience in at least one other Oracle product family.

    This credential recognizes professionals who create configuration designs and implement the Oracle Mobile Security Suite. Candidates must have a working knowledge of Oracle Mobile Security Suite Access Server, Oracle Mobile Security Suite Administrative Console, Oracle Mobile Security Suite Notification Server, Oracle Mobile Security Suite Containerization and Oracle Mobile Security Suite Provisioning and Policies. They must also know how to deploy the Oracle Mobile Security Suite.

    Although the certification is designed for Oracle PartnerNetwork members, it is available to any candidate. Successful completion of one exam is required.

    Source: Oracle Mobile Security Certification

    RSA Archer Certified Administrator (CA)Prerequisites: None required; Dell EMC highly recommends RSA training and two years of product experience as preparation for the RSA certification exams.

    Dell EMC offers this certification, which is designed for security professionals who manage, administer, maintain and troubleshoot the RSA Archer Governance, Risk and Compliance (GRC) platform.

    Candidates must pass one exam, which focuses on integration and configuration management, security administration, and the data presentation and communication features of the RSA Archer GRC product.

    Source: Dell EMC RSA Archer Certification

    RSA SecurID Certified Administrator (RSA Authentication Manager 8.0)Prerequisites: None required; Dell EMC highly recommends RSA training and two years of product experience as preparation for the RSA certification exams.

    Dell EMC offers this certification, which is designed for security professionals who manage, maintain and administer enterprise security systems based on RSA SecurID system products and RSA Authentication Manager 8.0.

    RSA SecurID CAs can operate and maintain RSA SecurID components within the context of their operational systems and environments; troubleshoot security and implementation problems; and work with updates, patches and fixes. They can also perform administrative functions and populate and manage users, set up and use software authenticators, and understand the configuration required for RSA Authentication Manager 8.0 system operations.

    Source: Dell EMC RSA Authentication Manager Certification

    RSA Security Analytics CAPrerequisites: None required; Dell EMC highly recommends RSA training and two years of product experience as preparation for the RSA certification exams.

    This Dell EMC certification is aimed at security professionals who configure, manage, administer and troubleshoot the RSA Security Analytics product. Knowledge of the product's features, as well the ability to use the product to identify security concerns, are required.

    Candidates must pass one exam, which focuses on RSA Security Analytics functions and capabilities, configuration, management, monitoring and troubleshooting.

    Source: Dell EMC RSA Security Analytics

    Advanced information technology security certifications 

    CCIE SecurityPrerequisites: None required; three to five years of professional working experience recommended.

    Arguably one of the most coveted certifications around, the CCIE is in a league of its own. Having been around since 2002, the CCIE Security track is unrivaled for those interested in dealing with information security topics, tools and technologies in networks built using or around Cisco products and platforms.

    The CCIE certifies that candidates possess expert technical skills and knowledge of security and VPN products; an understanding of Windows, Unix, Linux, network protocols and domain name systems; an understanding of identity management; an in-depth understanding of Layer 2 and 3 network infrastructures; and the ability to configure end-to-end secure networks, as well as to perform troubleshooting and threat mitigation.

    To achieve this certification, candidates must pass both a written and lab exam. The lab exam must be passed within 18 months of the successful completion of the written exam.

    Source: Cisco Systems CCIE Security Certification

    Check Point Certified Managed Security Expert (CCMSE)Prerequisites: CCSE certification R75 or later and 6 months to 1 year of experience with Check Point products.

    This advanced-level credential is aimed at those seeking to learn how to install, configure and troubleshoot Check Point's Multi-Domain Security Management with Virtual System Extension.

    Professionals are expected to know how to migrate physical firewalls to a virtualized environment, install and manage an MDM environment, configure high availability, implement global policies and perform troubleshooting.

    Source: Check Point CCMSE

    Check Point Certified Security Master (CCSM)Prerequisites: CCSE R70 or later and experience with Windows Server, Unix, TCP/IP, and networking and internet technologies.

    The CCSM is the most advanced Check Point certification available. This credential is aimed at security professionals who implement, manage and troubleshoot Check Point security products. Candidates are expected to be experts in perimeter, internal, web and endpoint security systems.

    To acquire this credential, candidates must pass a written exam.

    Source: Check Point CCSM Certification

    Certified SonicWall Security Professional (CCSP)Prerequisites: Attendance at an advanced administration training course.

    Those who achieve this certification have attained a high level of mastery of SonicWall products. In addition, credential holders should be able to deploy, optimize and troubleshoot all the associated product features.

    Earning a CSSP requires taking an advanced administration course that focuses on either network security or secure mobile access, and passing the associated certification exam.

    Source: SonicWall CSSP certification

    IBM Certified Administrator -- Tivoli Monitoring V6.3Prerequisites: Security-related requirements include basic knowledge of SSL, data encryption and system user accounts.

    Those who attain this certification are expected to be capable of planning, installing, configuring, upgrading and customizing workspaces, policies and more. In addition, credential holders should be able to troubleshoot, administer and maintain an IBM Tivoli Monitoring V6.3 environment.

    Candidates must successfully pass one exam.

    Source: IBM Tivoli Certified Administrator

    Master Certified SonicWall Security Administrator (CSSA)The Master CSSA is an intermediate between the base-level CSSA credential (itself an intermediate certification) and the CSSP.

    To qualify for Master CSSA, candidates must pass three (or more) CSSA exams, and then email training@sonicwall.com to request the designation. There are no other charges or requirements involved.

    Source: SonicWall Master CSSA

    Conclusion 

    Remember, when it comes to selecting vendor-specific information technology security certifications, your organization's existing or planned security product purchases should dictate your options. If your security infrastructure includes products from vendors not mentioned here, be sure to check with them to determine if training or certifications on such products are available.

    About the author:Ed Tittel is a 30-plus year IT veteran who's worked as a developer, networking consultant, technical trainer, writer and expert witness. Perhaps best known for creating the Exam Cram series, Ed has contributed to more than 100 books on many computing topics, including titles on information security, Windows OSes and HTML. Ed also blogs regularly for TechTarget (Windows Enterprise Desktop), Tom's IT Pro and GoCertify.


    Using Artificial Intelligence to Search for Extraterrestrial Intelligence | killexams.com real questions and Pass4sure dumps

    The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017. Nearly 75 participants, with a wide range of backgrounds from industry and academia, worked in teams on the project. The top team achieved a signal classification accuracy of 95%. The code challenge was sponsored by IBM, Nimbix Cloud, Skymind, Galvanize, and The SETI League.

    The ML4SETI project challenged participants to build a machine-learning model to classify different signal types observed in radio-telescope data for the search for extra-terrestrial intelligence (SETI). Seven classes of signals were simulated (and thus, labeled), with which citizen scientists trained their models. We then measured the performance of these models with tests sets in order to determine a winner of the code challenge. The results were remarkably accurate signal classification models. The models from the top teams, using deep learning techniques, attained nearly 95% accuracy in signals from the test set, which included some signals with very low amplitudes. These models may soon be used in daily SETI radio signal research.

    Three of the 42 offset Gregorian, 6-meter dishes that make up the Allen Telescope Array at the Hat Creek Radio Observatory in northern California.

    Deep learning models trained for signal classification may significantly impact how SETI research is conducted at the Allen Telescope Array, where the SETI Institute conducts its radio-signal search. More robust classification should allow researchers to improve the efficiency of observing each star system and allow for new ways to implement their search.

    Brief explanation of SETI data and its acquisition

    In order to understand the code challenge and exactly how it will help SETI research, an understanding of how the SETI Institute operates is needed. In this section, we’ll briefly go over the data acquisition of real SETI data from 2013–2015, the real-time analysis, and how it has been analyzed later in the context of the SETI+IBM collaboration. Some of this information can be found on the SETI Institute’s public SETI Quest page.

    Time-Series radio signals

    The Allen Telescope Array is an array of 42 six-meter-diameter dishes that observe radio signals in the 1–10 GHz range. By combining the signals from different dishes, in a process called “beamforming”, observations of radio signals from very small windows of the sky about specific stellar systems are made. At the ATA, three separate beams may be observed simultaneously and are used together to make decisions about the likelihood of observing intelligent signals. On the SETIQuest page, one can see the current observations in real-time.

    Screen capture from https://setiquest.info showing 3 beams under observation.

    The analog voltage signals measured from the antenna are mixed (demodulated) from the GHz range down to lower frequencies and then digitized. The output of this processing is a stream of complex-valued time-series data across a range of frequency bandwidths of interest. At any given moment, the ATA can observe 108 MHz of spectrum within the 1 to 10 GHz range.

    The software that controls the data acquisition system, analyzes the time-series data in real-time, directs repeated observations, and writes data out to disk is called SonATA (SETI on the ATA).

    To find signals, the SonATA software calculates the signal power as a function of both frequency and time. It then searches for signals with power greater than the average noise power that persist for more than a few seconds. The representation of the power as a function of frequency and time are called spectrograms, or “waterfall plots” in the parlance of the field. To compute a spectrogram, a long complex-valued time-series data stream is chunked into multiple samples of about one-second worth of data. For each of these one-second samples, signal processing is applied (Hann windowing) and the power spectrum is calculated. Then, the power spectrum for each one-second sampled are ordered next to each other to produce the spectrogram. This is explained in pictures in a talk I gave earlier this spring (see slides 7–13).

    Signal observed at the Allen Telescope Array from the Cassini satellite while orbiting Saturn on September 3, 2014.

    The figure above is an example of a classic “narrowband” signal, which is what SonATA primarily searches for in the data. The power of the signal is represented on a black & white scale. You can clearly see a signal starting at about 8.429245830 GHz and drifting up to 8.429245940 GHz over the ~175 second observation. Narrowband signals that have a large amount of power at a specific frequency (and hence, they have a “narrow” bandwidth) . The reason that SonATA searches for these signals is because this is the kind of signal we use to communicate with our satellites, and it’s how we suspect an E.T. civilization might transmit a signal to us if they were trying to get our attention. The central (“carrier”) frequency of a narrowband signal, however, is not constant. Due to the rotation of the Earth and to the acceleration of the source, the frequency of the received signal drifts as a function of time, called Doppler Drift (not to be confused with Doppler Shift, though they are related).

    The SonATA system was constructed to search primarily for narrowband signals. SonATA may label a signal as a “Candidate” when those narrowband characteristics are observed, the signal does not appear to have originated from a local source, and is not found in a database containing known RFI signals. After a signal has been labeled as a Candidate, a new set of observations are made to test if that signal is persistent.

    A persistent signal is one of the most important characteristics of a potential ET signal. First, SonATA tests to make sure it doesn’t see the same Candidate signal in the other two beams (which would indicate RFI). It then forms a beam at a different point in the sky to ensure that it doesn’t see the signal elsewhere. Then it looks back again to the same location. If it finds a signal again, the process is repeated. Each step along the way, the observed signal is recorded to disk in small files in an 8.5 kHz bandwidth about the frequency of the observation (as opposed to saving the entire stream of data over the full 108 MHz bandwidth). This pattern of observation can repeat up to five times, at which point the system places a phone call to a SETI researcher! (This has only happened once or twice in the past few years at the SETI Institute’s ATA, I’m told.) The “How Observing Works” link on the http://setiquest.info website explains this in more detail.

    While SonATA is trained to find narrowband signals, it will often trigger on other types of signals as well, especially if there is a large power spike. There are many different “classes” of signals with a range of characteristics, such as smoothly varying drift rates, stochastically varying drift rates and various amplitude modulations. Additionally, these characteristics vary in intensity (they can be more or less pronounced) in such a way that, overall, the different classes are not entirely distinguishable. Of course, this makes it hard to group and classify many of the real types of signals that are observed in SETI searches.

    Clustering and classifying real SETI data

    In 2015, the IBM Emerging Technologies jStart group joined up with researchers from the SETI Institute, NASA, and Swinburne University, forming this collaboration. The goal was two-fold: exercise some of IBM’s new data management (Object Storage) and analytics (Apache Spark) product offerings to gain feedback, while providing significant computational infrastructure for SETI and NASA to explore the SETI raw data set. The 2013–2015 data set from the SETI Institute, which contains over 100 million Candidate and RFI observations and is a few TB in size, was transferred to IBM Object Storage instances. The Object Storage instances are located within the same data center as an IBM Enterprise Spark Cluster that was provisioned specifically for this collaboration. This computational setup has allowed researchers to spin through the data set many times over, searching for patterns in the observations. This data set is publicly available to citizen scientists via the SETI@IBMCloud project.

    Over the following year, multiple attempts were made to cluster and classify the subset of Candidate signals found in the full data set. Some approaches were found to be more robust than others, but none were quite satisfactory enough for SETI Institute scientists to employ those techniques on a regular basis as part of their standard observational program.

    Simulated signals and their classifiers

    Due to the challenge of clustering and classifying the real SETI Candidate data, we decided to build a set of simulated signals that we could control and label. With a labeled set of data, we, or others, could train models for classification.

    Based on manual observation, there are a number of classes of signals that SETI Institute researchers often observe. For this work, we decided to focus on just six of the different classes, plus a noise class. The signal classes were labeled ‘brightpixel’, ‘narrowband’, ‘narrowbanddrd’, ‘noise’, ‘squarepulsednarrowband’, ‘squiggle’, and ‘squigglesquarepulsednarrowband’. The class names are descriptive of their appearance in a spectrogram.

    All simulations were a sum of a signal and a noise background. They are described in detail below in order of increasing complexity. Be aware that all simulations were done entirely in the time-domain. The output data files were complex-valued time-series. All noise backgrounds were randomly sampled gaussian white noise with a mean of zero and RMS width of 13.0 for both the real and imaginary component. The spectrogram in the figures below were produced from a few example simulations. Also, the formula displayed in the figures do not fully characterize the simulations, but they are qualitatively useful for discussion.

    Gaussian white-noise with no signal. Noise

    The simulations labeled as ‘noise’, contained no signal, A(t)=0, plus the gaussian white noise background. In the full data set, there were 20k “noise” simulations.

    Typical narrowband signal with drifting central frequency. Narrowband

    Narrowband signals begin at some initial frequency, f₀, then change over time with a constant drift rate, d. Frequency drift indicates a non-zero acceleration between the transmitter and receiver. The amplitudes of these signals are constant throughout the simulation, A(t) = C. We simulated 20k narrowband signals, each one with a randomly selected initial frequency, fo, drift rate, d, and signal amplitudes, C.

    Narrowband DRD

    Sometimes, signals are observed at the ATA where the drift rate does not remain constant. The frequency of the signal not only shifts in time, but shifts with an increasing or decreasing rate, as seen in the figure. These are labeled “narrowbanddrd”, where DRD stands for “drift rate derivative”. We simulated 20k narrowbanddrd signals, each one with a randomly selected initial frequency, fo, drift rate, d, drift rate derivative, “d-dot”, and signal amplitude, C.

    SquarePulsedNarrowBand

    Another phenomenon observed in ATA data are narrowband signals that appear to have a square-wave amplitude modulation. The square-wave amplitude modulation, A(t), is parameterized by its periodicity, P, duty cycle, D, and initial start time t_phi. Again, we simulated 20k signals of this type. The six variables that characterize these signals, fo, d, C, P, D and t_phi, were randomly chosen for each simulated signal.

    Squiggles

    Signals with stochastically-varying frequencies often show up in ATA data, and are known as ‘squiggles’. These signals were simulated by assigning an amplitude, s, to a randomly sampled value between -1 and 1. This simulates the random-walk of the signal’s frequency as observed in the data. Note that the equation for the frequency as a function of time is slightly different here in order to describe the randomly shifting frequency. We simulated 20k squiggles with randomly chosen values for fo, d, C and s.

    SquiggleSquarePulsedNarrowBand

    We added a square-wave amplitude modulation to the squiggle signals in the same way was was applied to the narrowband. We simulated 20k squiggles with randomly chosen values for fo, d, C, s, P, D and t_phi. (The title of this signal is a bit inconsistent in structure with the others because it contains the word “narrowband”. A more appropriate signal name would have been SquarePulsedSquiggle.)

    BrightPixels

    Finally, signals called “brightpixels” were simulated. These are small blips of a signal where significant power is found for just a very short time at a specific frequency. In the real data at the ATA, however, these signals sometimes have broader spectrum. These are simulated in the exact same way as “squarepulsednarrowand”, but with restricted range of values for the parameters that control the square-wave modulation. In particular, the periodicity, P, is fixed to the total length of the simulation, T. The duty cycle, D, is restricted to a very narrow range, which lets the bright pixel vary in size slightly. The initial offset time, t_phi, has the same full range as before. For the 20k simulated brightpixel signals, the fo, d, and C parameters were randomly selected, as before.

    Distinguishability of signals: Range of simulation parameter values

    The simulation parameters were confined to a particular range of values in order to create a simulated data set that closely represents the range of values found in real signals observed at the ATA. The amplitude of the background noise in all simulations was fixed by a gaussian distribution with zero mean and standard deviation width of 13.0 for both the real and imaginary components. This amplitude matches the typical noise amplitude observed at the ATA. All non-noise signals were simulated with an amplitude equal to a fraction of this noise amplitude for both the real and imaginary components.

    Four ‘squiggle’ simulations with different values for the squiggle parameter, s, and different SNRs. The simulation at the bottom right is not visible by the human eye.

    The amplitudes, C, were uniformly distributed between 0.65 to 6.5 for most signals — brightpixel amplitudes ranged slightly higher, up to 9.75. Respectively, the range of signal to noise ratios, SNR, were, [0.05, 0.5] and [0.1,0.75]. Note that these are signal and background amplitude values in the time-domain, and not in the spectrograms.

    Parameters that controlled particular characteristics of the signals were uniformly distributed from nearly zero to values that produced a maximum effect similar to that observed in the real data. For example, the squiggle parameter, s, was distributed in the range [0.0001, 0.005]. As s approaches zero, squiggles begin to resemble narrowband signals. Similarly, the drift rate derivate value was evenly distributed in the range of [0.01, 0.08]. As the derivative approaches zero, these signals become indistinguishable from narrowband signals. In this particular case, we purposefully kept the lower-bound significantly above zero in order to keep this class of signal more distinguishable from narrowband.

    For the square-wave amplitude modulation, the periodicity, P, was uniformly distributed from 15.6% to 46.9% of the total simulation time, T. The duty cycle, D, which controls the width of the square-wave, was uniformly distributed from 15% to 80% of the chosen periodicity, P. In order to simulate brightpixels, we used square-wave amplitude modulation with a fixed periodicity, P=T, and a very restricted duty cycle, D=[0.78%, 3.125%].

    Simulation software & infrastructure

    Simulation software was written in Java and Scala and executed on an 30-executor IBM Enterprise Spark cluster. Data were written to IBM Object Storage and IBM Db2 (formerly dashDB), both located within the same SoftLayer datacenter. There is no recorded simulation performance data, but anecdotally, about 1000 simulations could be created per minute, with the primary bottleneck being I/O to Object Storage and Db2. The software we used to simulate the SETI signals is still in a private repository. However, in the near future we will apply an Apache 2.0 License and release that code for those who are interested.

    Training and test set details

    In total, 140k signals were simulated and available for training classification models. Each simulated signal was placed in an individual file. Each file contained a JSON header, followed by raw bytes for the complex-valued time-series data. The ibmseti Python package, which may be used to read and analyze real data from the ATA, was extended to read these simulation data files, facilitate signal processing and produce spectrogram. In the training data, the JSON headers contained the signal classification value and a UUID, whereas the JSON headers for the test data only contained a UUID. The UUIDs were used for reporting a team’s test scores.

    Two test sets were available for teams to score their trained models. The first test set, which we called the “preview” test set, allowed teams to score their models publicly. The second test set, called the “final” test set, was used for the final scoring and judging of classification models.

    Each test set contained about 2400 simulated signals. However, the exact number of simulated signals for each class in the test sets were different. There were approximately 350 +- 50 simulated signals of each class. An unequal number of samples per class prevented attempts at artificially improving a team’s score. If there was an equal number of samples per class, and teams became aware of this, that constraint could be exploited to modify class estimators and boost scores.

    Teams were asked to build a .csv file scorecard. Each row of the scorecard file contained the UUID of the simulated file in the first position, along with seven numerical values that represented their model’s degree of belief or probability for each class. The order of the values in each row were required to follow the alphabetical ordering of the class labels: brightpixel, narrowband, narrowbanddrd, noise, squarepulsednarrowband, squiggle, squigglesquarepulsednarrowband. For example, the line below indicates that a model scored the simulation test file “dbe38b359e70efb1a5fc2ea7bc4c619c”, with a 99.997% probability of being a brightpixel.

    Teams then submitted their scorecard for either the Preview or Final test set to the respective online scoreboard. Teams were allowed six submissions to the Preview Scoreboard, which allowed models to be updated and compared with other participants. However, only one submission was allowed to the Final Scoreboard. The scoreboards calculated the multinomial logistic regression loss (LogLoss) for the scorecard, which was the team’s score. The team with the lowest LogLoss value was declared the winner.

    The winning teams and results

    All participants of the code challenge produced excellent results. Overall, they were much better than expected. The top teams were able to detect and identify signals that were buried fairly deep into the noise.

    The winning team, ‘Effsubsee’ (F_c), is Stéphane Egly, Sagar Vinodababu and Jeffrey Voien. They posted a classification accuracy of 94.99%! The second place team was, ‘Signet’, who is Benjamin Bastian. He posted a classification accuracy of 94.67%. These teams differed only in their classification of a handful of the test cases.

    Below are the classification accuracies and LogLoss scores for their models with the preview test set (scores for the final test set won’t be published). In addition, an accompanying confusion matrix for each team’s preview test set scorecard can be found in a Jupyter notebook in the ML4SETI repository.

    Effsubsee’s precision, recall and f1 scores for the ML4SETI Preview Test Set. Classification accuracy is equal to the average recall score. Signet’s precision, recall and f1 scores for the ML4SETI Preview Test Set. Classification accuracy is equal to the average recall score.

    Interestingly, you’ll notice, Effsubsee’s LogLoss score for the preview test set was lower than Signet’s score. However, Signet’s classification accuracy was slightly greater.

    Following Effsubsee and Signet, were Snb1 (Gerry Zhang) with 87.5% classification accuracy and LogLoss of 0.38467, Signy McSigface (Kevin Dela Rosa and Gabriel Parent) with 83.9% classification accuracy and LogLoss of 0.46575, and NulliusInVerbans with 82.3% classification accuracy and LogLoss of 0.56032. Their LogLoss scores are found on the Final Scoreboard.

    First place and runner-up classification models

    The Effsubsee and Signet teams have provided documentation and released their models under the Apache 2.0 license on GitHub.

    Top Team: Effsubsee (this section was written by Team Effsubsee)

    Our approach was to experiment with various leading image classification architectures, and systematically determine the architecture that works best for the SETI signal data. We split the data into 5 parts, or “folds”, with equal class distributions. Each model was trained on 4 folds, and the accuracy against the 5th fold was measured. (This is called the validation accuracy.) Below are the architectures that were constructed and the best validation accuracies we achieved for each class of architecture.

    Residual Networks with 18, 50, 101, 152, 203 layers. The best model was the ResNet-101, with a single-fold validation accuracy of 94.99%.

    Wide Residual Networks with 34x2, 16x8, 28x10 layers(x)expansion-factors. The best model was the WideResNet-34x2, with a single-fold validation accuracy of 95.77%.

    Dense Networks with 161, 201 layers. The best model was the DenseNet-201, with a single-fold validation accuracy of 94.80%.

    Dual Path Networks with 92, 98, 131 layers. The best model was the DPN-92, with a single-fold validation accuracy of 95.08%.

    With very deep architectures, a common problem is overfitting to the training data. This means that the network will learn very fine patterns in the training data that may not exist in real-world (or test) data. While each of the five single-fold WideResNet-34x2 models had the highest validation accuracies, it was slightly overfitting to the training data. In contrast, a single-fold ResNet-101 performed the best on the preview test set, outperforming each of the other single-fold models. (This also makes the single-fold ResNet-101 an attractive candidate in a scenario where there are significant time constraints for prediction.)

    However, for the winning entry, we used an averaged ensemble of five Wide Residual Networks, trained on different sets of 4(/5) folds, each with a depth of 34 (convolutional layers) and a widening factor of 2; the WideResNet-34x2.

    In order to avoid overfitting, we combined the five single-fold WideResNet-34x2 in such a way that it takes a majority vote between them and eliminates inconsistencies. This was accomplished by a simple average the five results. As a result, the log-loss score for the five-fold WideResNet-34x2 was considerably better than the single-fold ResNet-101, with scores of 0.185 and 0.220, respectively.

    In addition to their code, team Effsubsee placed the set of five model parameters in their GitHub repository. You can try the model yourself to calculate the class probabilities for a simulated signal, as demonstrated in this Jupyter notebook in IBM’s Data Science Experience. (To use this notebook in your own DSX project, download the .ipynb file and create a new notebook from File.) Note that the Effsubsee original code was slightly modified in order to run their models on CPU. In general, with most modern deep learning libraries, this is relatively simple to achieve.

    Second Place: Signet

    Signet used a single Dense Convolutional Neural Net with 201 layers, as implemented in the torchvision module of pytorch. This was an architecture also explored by Effsubsee. It took approximately two days to train the model on Signet’s GeForce GTX 1080 Ti GPU. Signet’s code repository is found on GitHub.

    Signet’s model is also demonstrated calculating a simulated signal’s class probabilities in a Jupyter notebook on IBM Data Science Experience. Some of Signet’s code was slightly modified to run on CPU. (To use this notebook in your own DSX project, you can download the .ipynb file and create a new notebook from File.)

    Run on GPU

    Of course, you can also run these models locally or on a cloud server, such as those offered by IBM/SoftLayer or Nimbix Cloud, with or without a GPU. The setup instructions are rather simple, especially if you install Anaconda. But even without Anaconda, you can get away with pip installing almost everything you need. First, however, you will need to need to install CUDA 8.0 and should install cuDNN. After that, assuming you’ve installed Anaconda, it should be a handful of steps to get up and running.

    Conclusions & next steps

    The ML4SETI Code Challenge has resulted in two deep learning models with a demonstrated high signal classification accuracy. This is a promising first step in utilizing deep learning methods in SETI research and potentially other radio-astronomy experiments. Additionally, this project and the DSX notebooks above offer a clear picture of how a deep learning model, trained on GPUs, can then be deployed into production on CPUs when only inference about future new data need to be calculated.

    The next most immediate task to be taken by the SETI/IBM team and the winning code challenge team, Effsubsee, will be to write an academic paper and to present this work at conferences. A future article will appear on arxiv.org and potentially in a suitable astro-physics journal.

    Future technical updates

    There are some improvements on this work that could be done to build more robust signal classification models.

    New signal types & characteristics

    There are two obvious advancements that can be made to train new deep learning models. First, more signal types can be added to the set of signals we simulate. For example, a sine-wave amplitude modulation could be applied to narrowband and squiggles, brightpixels could be broadened to include a wider range of frequencies, and amplitude modulation could be applied to narrowbanddrd. Second, the range of values for parameters that control the characteristics of the simulations could be changed. We could use smaller values for the squiggle parameter, and drift rate derivatives, for example. This would make some of the squiggle and narrowbanddrd signals appear very much like the narrowband signals. Obviously we expect classification models to become confused, or to identify those as narrowband more frequently as the parameters go to zero. However, it would be interesting to see the exact shape of the classification accuracy as a function of the amplitude of the parameters that control the simulations.

    Different background model

    We originally intended to use real data for the background noise. We observed the Sun over a 108 MHz bandwidth window and recorded the demodulated complex-valued time-series to disk. Overall there was an hour of continuous observation data. For the code challenge data sets, we used gaussian white noise, as described above. This was the version 3 (v3) data set. However, the version 2 data (v2) set does use the Sun observation as the background noise. The Sun noise significantly increases the challenge of building a signal classifiers because the background noise is non-stationary and may contain random blips of signal of appreciable power.

    The Sun noise could be used instead of gaussian white noise, along with the expanded ranges of signal characteristics in a future set of simulated data.

    Object detection with multiple signals

    We would like to perform not just signal classification, but be able to find multiple different classes of signals in a single observation. The real SETI data from the ATA often contains multiple signals, and it would be very helpful to identify as many of these signal classes as possible. In order to do this, we’d need to create a labeled data set specifically for the purpose for training object detection models. In principle, all of the components in the simulation software exist already to build such a data set.

    Signal characteristic measurements and prediction

    A useful addition to deep learning models would be the ability to measure characteristics of the signal. The SonATA system can estimate a signal’s overall power, starting frequency and drift rate. Could deep learning systems go beyond that, especially for signals that are not the standard narrowband, and measure quantities that represent the amount of squiggle, the average change in the drift rate, or parameters about the amplitude modulation? The simulation software would need to be significantly updated in order to build such a system. The simulation signals would also need to include, beside the class label, the signal amplitude, frequency, drift rate, squiggle amplitude, etc., in order for machine learning models to learn how to predict those quantities. One solution may even be to perform signal classification with deep learning, and then use a more standard physics approach and perform a maximum likelihood fit to the signal to extract those parameters.

    ML4SETI Code Challenge reboot

    Even though the code challenge is officially over, it’s not too late to attain the code challenge simulation data and build your own model. We’ve left the data available in the same locations as before, and the Preview and Final test sets and scoreboards are still online. You can form a team (or work on your own) and submit a result for the foreseeable future while these data remain publicly available. Additionally, you can join the ML4SETI Slack team to ask questions from me, SETI researchers, the top code challenge teams, and other participants.

    There are a few places to get started. First, it may be informative and inspiring to watch the Hackathon video recap. Second, you should visit the ML4SETI github repository and read the Getting Started page, which will direct you to the data sets and basic introduction on how to read them and produce spectrogram. Finally, you could take the example code above from Effsubsee and Signet and iterate on their results. Let us know if you beat their scores!

    Acknowledgements

    The ML4SETI code challenge would not have happened without the hard work of many people. They are Rebecca McDonald, Gerry Harp, Jon Richards, and Jill Tarter from the SETI Institute; Graham Mackintosh, Francois Luus, Teri Chadbourne, and Patrick Titzler from IBM. Additionally, thanks to Indrajit Poddar, Saeed Aghabozorgi, Joseph Santarcangelo and Daniel Rudnitski for their help with the hackathon and building the scoreboards.


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    No result found, try new keyword!That's a potential hazard from a memory management ... "I" for IBM's trace file as seen in Figure 1. We can see in Figure 2 the analysis result and recommendation virtually instantly. Jinwoo Hwang is ...


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