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Data Analytics in Cyber Security
The document discusses the integration of big data analytics in cyber security, highlighting current challenges and shortcomings of traditional security information and event management systems. It emphasizes the need for improved data processing capabilities, introducing concepts such as data pipelines and big data structures. The speaker, Shomiron Das Gupta, presents opportunities in the field, including managed security services and threat hunting.
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Data Analytics in Cyber Security
- 1. © Copyright 2017NETMONASTERY Inc Big Data Structures in the Cyber Security Puzzle 1 Shomiron DAS GUPTA, Founder, CEO NETMONASTERY Inc. Data Analytics in Cyber Security
- 2. © Copyright 2017NETMONASTERY Inc Agenda … Setting Expectations! 2 NEXT 45 MINS 1. Cyber Security - How it works, today! 2. Data Analytics, the What and the Why 3. The Technical Aspects 4. The Pipeline 5. Opportunities - Gaps we’re aiming for 6. Demo
- 3. © Copyright 2017NETMONASTERY Inc Hello, who is speaking? 3 Founder of NETMONASTERY, we built DNIF - An Integrated Threat Hunting Platform for the CSOC Research on Detection, Hunting and …. ML One of the few guys that does defense for a living GCIA 2000 - 18Yrs of Intrusion Detection, Handling WHAT I DO FOR A LIVING @shomiron
- 4. © Copyright 2017NETMONASTERY Inc Cyber Security — The NUMBERS! 4 THE CURRENT WORLD CHALLENGES ■ Data growth 230% YoY ■ Mid Sized Enterprise - 30 TB Monthly ■ Enterprise Grade - 100+ TB Monthly ■ Government - 1.23 PB Monthly ■ Process, Percolation / Lag < 10s ■ Detect stuff we didn’t know about
- 5. © Copyright 2017NETMONASTERY Inc Cyber Security Solutions — Where were we? 5 THE CURRENT WORLD Security Information and Event Management ■ It’s… last generation ■ Slow… can’t scale (database) ■ Rule based - KNOWN / KNOWNs ■ Beautiful UI - HiFi ■ Center of the Earth
- 6. © Copyright 2017NETMONASTERY Inc 6 We FAIL* EVERYDAY!!
- 7. © Copyright 2017NETMONASTERY Inc Data Analytics + Big Data Structures 7 AND THE MARRIAGE PARTY 1. Facets and dimensionality 2. Static / dynamic analytics 3. Why static is better 4. Then why are we doing dynamic? 5. The Marriage of Big Data with Analytics 6. Hadoop…. anybody?
- 8. © Copyright 2017NETMONASTERY Inc Technical Aspects 8 HOW IT WORKS 1. Horizontal scaling - Speed 2. Pipeline, let’s revisit Hadoop? 3. Data preparation - Parsing / Annotation 4. Enrichment - localization for context 5. Plugin framework - SOAR - Enrich, Validate, Respond 6. AI / ML ;) 7. Platform + Content
- 9. © Copyright 2017NETMONASTERY Inc 9 The PIPELINE DATA FLOW WITHIN COLLECT PARSE ANNOTATE ENRICH VISUALIZE CORRELATE INDEX VALIDATE RESPOND REMEDIATE
- 10. © Copyright 2017NETMONASTERY Inc 10 The PIPELINE DATA FLOW WITHIN ADAPTER DATASTORE CORRELATOR
- 11. © Copyright 2017NETMONASTERY Inc Talking about DNIF 11 AND COMPETITION 1. SIEM - ArcSight, QRadar 2. Splunk, ELK 3. Hadoop (Cloudera, Hortonworks), Tableau, HANA, Watson 4. Free Parsers, SOAR, Packages / Plugins 5. Power of Collaboration
- 12. © Copyright 2017NETMONASTERY Inc Opportunities - Gaps we’re aiming for 12 THE BUSINESS ANGLE, YAY! 1. Managed Security Services 2. Threat Hunting 3. Managed Detection Response 4. SWIFT, IMPS, NEFT, CC, Exchange, Telecom Switching 5. The Data Lake Theory - WORKBENCH
- 13. Thank You Oh BTWWe are hiring Write to us - hello@dnif.it 13