|Exam Name||:||IBM Information Management DB2 10 Technical Mastery Test v3|
|Questions and Answers||:||35 Q & A|
|Updated On||:||March 22, 2019|
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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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In September 2018, IBM introduced a new product, IBM Db2 AI for z/OS. This synthetic intelligence engine monitors facts access patterns from executing SQL statements, uses laptop getting to know algorithms to pick out most effective patterns and passes this suggestions to the Db2 question optimizer to be used with the aid of subsequent statements.desktop learning on the IBM z Platform
In may also of 2018, IBM announced version 1.2 of its desktop getting to know for z/OS (MLz) product. this is a hybrid zServer and cloud utility suite that ingests performance data, analyzes and builds fashions that symbolize the health popularity of a variety of symptoms, screens them over time and gives true-time scoring capabilities.
a number of facets of this product offering are geared toward supporting a neighborhood of model builders and bosses. as an example:
This machine researching suite become firstly geared toward zServer-based mostly analytics functions. one of the vital first glaring choices was zSystem performance monitoring and tuning. equipment administration Facility (SMF) statistics which are automatically generated by using the working device provide the raw information for gadget useful resource consumption reminiscent of relevant processor utilization, I/O processing, memory paging etc. IBM MLz can assemble and keep these statistics over time, and construct and instruct models of gadget conduct, ranking those behaviors, establish patterns now not without difficulty foreseen by way of people, enhance key efficiency indicators (KPIs) after which feed the mannequin effects back into the equipment to have an effect on system configuration alterations that may increase efficiency.
The next step become to put into effect this suite to research Db2 efficiency statistics. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the laptop discovering technology to Db2 operational information to gain an figuring out of Db2 subsystem fitness. it may well dynamically build baselines for key performance symptoms, give a dashboard of these KPIs and provides operational group of workers precise-time perception into Db2 operations.
while prevalent Db2 subsystem efficiency is a crucial component in usual software fitness and performance, IBM estimates that the DBA support workforce spends 25% or greater of its time, " ... fighting entry route issues which trigger efficiency degradation and repair affect.". (See Reference 1).AI involves Db2
accept as true with the plight of modern DBAs in a Db2 ambiance. In state-of-the-art IT world they should support one or extra massive information functions, cloud application and database services, application installation and configuration, Db2 subsystem and application performance tuning, database definition and management, catastrophe recuperation planning, and extra. question tuning has been in existence considering the fact that the origins of the database, and DBAs are continually tasked with this as smartly.
The coronary heart of question direction analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the records, reports the locations of the objects to be accessed and develops a listing of candidate records access paths. These access paths can consist of indexes, desk scans, numerous table be a part of methods and others. within the information warehouse and massive statistics environments there are continually extra selections attainable. One of these is the existence of summary tables (every so often known as materialized query tables) that include pre-summarized or aggregated information, consequently permitting Db2 to evade re-aggregation processing. a different choice is the starjoin access path, normal within the statistics warehouse, where the order of table joins is modified for performance explanations.
The Optimizer then stories the candidate entry paths and chooses the access course, "with the lowest cost." cost in this context capability a weighted summation of resource utilization together with CPU, I/O, memory and different supplies. at last, the Optimizer takes the bottom cost entry course, retailers it in reminiscence (and, optionally, within the Db2 directory) and starts off access path execution.
big data and statistics warehouse operations now include utility suites that allow the business analyst to use a graphical interface to construct and manipulate a miniature facts mannequin of the information they want to analyze. The programs then generate SQL statements based on the users’ requests.
The issue for the DBA
so as to do respectable analytics in your assorted facts outlets you want a pretty good figuring out of the information requirements, an understanding of the analytical capabilities and algorithms available and a high-efficiency records infrastructure. regrettably, the quantity and placement of records sources is expanding (both in size and in geography), records sizes are growing, and applications proceed to proliferate in quantity and complexity. How may still IT managers aid this environment, certainly with the most skilled and mature staff nearing retirement?
keep in mind also that a huge part of reducing the full cost of ownership of those programs is to get Db2 applications to run faster and greater efficiently. This always interprets into using fewer CPU cycles, doing fewer I/Os and transporting much less information throughout the community. on account that it is commonly tricky to even establish which functions might improvement from efficiency tuning, one approach is to automate the detection and correction of tuning considerations. here's the place desktop discovering and synthetic intelligence may also be used to superb effect.Db2 12 for z/OS and artificial Intelligence
Db2 edition 12 on z/OS makes use of the machine researching facilities outlined above to acquire and keep SQL question textual content and access path particulars, in addition to actual performance-linked old suggestions such as CPU time used, elapsed instances and effect set sizes. This providing, described as Db2 AI for z/OS, analyzes and outlets the facts in laptop learning fashions, with the mannequin evaluation effects then being scored and made purchasable to the Db2 Optimizer. The next time a scored SQL commentary is encountered, the Optimizer can then use the mannequin scoring facts as input to its access path choice algorithm.
The outcomes may still be a reduction in CPU consumption because the Optimizer uses mannequin scoring enter to choose improved entry paths. This then lowers CPU fees and speeds utility response instances. a big advantage is that the use of AI software doesn't require the DBA to have statistics science potential or deep insights into question tuning methodologies. The Optimizer now chooses the greatest entry paths primarily based no longer handiest on SQL question syntax and statistics distribution information however on modelled and scored old efficiency.
This can be notably essential if you shop data in diverse locations. as an example, many analytical queries in opposition t massive statistics require concurrent entry to certain information warehouse tables. These tables are commonly known as dimension tables, and that they contain the records features continually used to handle subsetting and aggregation. as an instance, in a retail atmosphere consider a table called StoreLocation that enumerates every keep and its area code. Queries towards shop earnings facts may need to combination or summarize earnings by means of location; therefore, the StoreLocation table should be used by some large records queries. during this ambiance it's standard to take the dimension tables and duplicate them continuously to the big facts application. in the IBM world this place is the IBM Db2 Analytics Accelerator (IDAA).
Now suppose about SQL queries from both operational purposes, facts warehouse clients and large information enterprise analysts. From Db2's point of view, all these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they should obviously be directed to entry the StoreLocation desk within the warehouse. on the other hand, the question from the company analyst against large data tables should probably access the copy of the desk there. This consequences in a proliferations of competencies entry paths, and extra work for the Optimizer. thankfully, Db2 AI for z/OS can provide the Optimizer the tips it must make sensible entry route choices.how it Works
The sequence of movements in Db2 AI for z/OS (See Reference 2) is generally the following:
There are also numerous consumer interfaces that supply the administrator visibility to the status of the accumulated SQL remark performance records and mannequin scoring.summary
IBM's computer gaining knowledge of for zOS (MLz) offering is getting used to excellent effect in Db2 edition 12 to enhance the efficiency of analytical queries as well as operational queries and their linked functions. This requires administration attention, as you ought to assess that your business is prepared to eat these ML and AI conclusions. How will you measure the prices and merits of using computer getting to know? Which IT aid body of workers ought to be tasked to reviewing the influence of model scoring, and maybe approving (or overriding) the outcomes? How will you assessment and justify the assumptions that the software makes about entry path selections?
In different phrases, how neatly do you know your records, its distribution, its integrity and your existing and proposed entry paths? this can determine where the DBAs spend their time in supporting analytics and operational utility efficiency.
# # #
John Campbell, IBM Db2 exclusive EngineerFrom "IBM Db2 AI for z/OS: boost IBM Db2 application efficiency with machine discovering"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-raise-ibm-db2-software-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/assist/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
IBM unveiled a brand new version of its flagship information integration product -- IBM InfoSphere tips Server 8.5 -- at its information on Demand conference remaining week in Las Vegas. big Blue also took the wraps off the latest version of its mainstay database management equipment, IBM DB2.
SearchDataManagement.com was at the conference and sat down with Bernie Spang, IBM’s director of tips administration product method, to get greater details concerning the new releases. Spang talked about the background of InfoSphere counsel Server and DB2’s new capabilities, and he defined one of the most the reason why IBM is so attracted to buying information warehouse appliance vendor Netezza. here are some excerpts from that dialog:
might you supply me a quick history lesson on the IBM InfoSphere product line?
Bernie Spang: It definitely has multifaceted origins. The statistics stage and exceptional stage, cleaning and ETL capabilities come from the Ascential acquisition a couple of years ago. The federation and replication capabilities which are a part of InfoSphere counsel Server have a heritage again in IBM below different names at different times.
What are one of the most new capabilities in InfoSphere suggestions Server 8.5?
Spang: one of the pleasing things in regards to the InfoSphere counsel Server is the device set that comes along with it for accelerating the construction of integration jobs, as well as new fast-track capabilities and new enterprise glossary capabilities [that] enable the collaboration between company and IT on what the meaning of facts is and the way it flows together.
what's the new InfoSphere Blueprint Director?
Spang: That gives clients the potential to capture the most appropriate practices for designing and constructing and laying out an integration job to ensure that you’re basically based on company wants and you’re pulling the correct assistance collectively unless they’re in the approach. It’s yet another layer of collaboration that we’ve built into the product, and it makes it possible for users to look the satisfactory metrics associated with each piece of records as it moves through the manner.
What does Blueprint Director appear to be to the end person?
Spang: It’s a visual atmosphere the place you’re laying out the combination and also you’re defining it and then you can use the quickly-track capability to generate the ETL jobs. It’s that visual toolset for defining your integration challenge. And it ties with the enterprise word list, where the enterprise users and IT are agreeing on the definition of terms.
What points have you ever introduced in the new version of DB2?
Spang: IBM DB2 edition 10 is a new product that we’re offering this week. [It offers] out-of-the-container efficiency advancements up to 40% for some workloads [and] improved scalability. The other enjoyable thing is a brand new skill that we’re calling DB2 time travel query – the capability to question assistance within the present, in the past and sooner or later. in case you’ve loaded information, like new pricing assistance for subsequent quarter, that you may do queries as if it had been next quarter. when you have business agreements or guidelines which are over a term, which you could do queries in the future and base it on how the guidelines should be in effect at that time. organizations already do this these days, however generally by using writing software code. by means of pushing it down into the database application, we’re drastically simplifying the system and tremendously cutting back the amount of code.
IBM is within the technique of acquiring Westboro, Mass.-primarily based data warehouse appliance seller Netezza and its container programmable gate array processor technology. What precisely is the price of this expertise?
Spang: Processing velocity is accomplishing the laws of physics [in terms of its] potential to proceed to grow, while on the equal time the should method more advice and do more transactions is starting to be unabated. So how do you get those next-generation efficiency advancements? you place the items collectively and enormously optimize them for particular workloads. That capacity you ought to have the software optimized for the hardware even all the way down to the processor degree. The box programmable gate array permits you to definitely application at a chip level, [and that leads to] a good deal greater speeds than having it written in software running on a accepted-purpose processor.
October 17, 2007 15:15 ET
built-in answer From IBM and Lighthouse Meets Regulatory Compliance Challenges
LAS VEGAS, NV--(Marketwire - October 17, 2007) - IBM guidance on Demand convention -- Northeast Utilities (NU), New England's biggest utility system, has chosen an built-in information management solution from IBM (NYSE: IBM) and Lighthouse computer capabilities, Inc., to satisfy its starting to be number of statistics management, e mail archiving and compliance requirements.
The integrated records management gadget will assist NU reply to litigation and e-discovery regulatory compliance necessities by using greater managing, securing, storing and archiving e mail messages and digital information.
"Northeast Utilities looks to continue the momentum relocating forward as our new records assistance management program evolves into a robust and a success program. The synergies constructed with our IBM business accomplice Lighthouse computing device functions, and our technically proficient in-condominium group, have enabled us to successfully set up and configure IBM's RM application equipment. we're laying down a robust basis to accomplish our strategic enterprise goals," talked about Greg Yatrousis, Northeast Utilities' IT Product manager.
The newly applied facts administration system is anticipated to decrease NU's working fees by means of decreasing the time and effort integral to retrieve guidance. The device also will support NU's facts and tips administration policies by means of picking out the category and layout of corporate statistics, monitoring compliance with business and criminal retention necessities for facts, selecting the custodians of listing courses, and implementing established security necessities and person access in line with criminal and business requirements.
The IBM software enabling NU to use counsel as a strategic asset contains: IBM DB2 content material supervisor, IBM DB2 data manager, IBM DB2 doc manager, IBM WebSphere tips Integration, IBM CommonStore, IBM DB2 content supervisor records Enabler, IBM content supervisor On Demand.
About Northeast Utilities
Northeast Utilities operates New England's biggest utility system serving greater than two million electric powered and natural gas customers in Connecticut, western Massachusetts and New Hampshire. NU has made a strategic resolution to center of attention on regulated business opportunities. For greater information visit www.nu.com
About Lighthouse desktop features
Lighthouse desktop services is a relied on IT marketing consultant to leading companies all through the northeast. Lighthouse is an IBM Premier business companion, and positioned quantity 228 in VARBusiness 2007 ranking of the appropriate 500 IT solution provider businesses within the country. Lighthouse is also winner of IBM's 2006 Beacon Award for typical Technical Excellence in a business companion. For more assistance consult with www.LighthouseCS.com.
For greater counsel on IBM's enterprise content administration choices, consult with http://www-306.ibm.com/utility/facts/cm/
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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:
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.
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.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:
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:
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 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:
Source: Symantec CertificationIntermediate 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 AnalyticsAdvanced 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 firstname.lastname@example.org to request the designation. There are no other charges or requirements involved.
Source: SonicWall Master CSSAConclusion
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.
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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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