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Technical track chris calvert-1 30 pm-issa conference-calvert


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Technical track chris calvert-1 30 pm-issa conference-calvert

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Technical track chris calvert-1 30 pm-issa conference-calvert

  1. 1. © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. ISSA Conference Chris Calvert, CISSP, CISM – Director of Solution Innovation
  2. 2. 2 My Job Is Innovation So I Own The Buzzword Slides (Google Trends Report)
  3. 3. 3 The Security Industry Is Not Catching Enough Bad GuysMost enterprises remain challenged with missing critical breaches. of business networks have traffic going to known malware hosting websites (Cisco 2014 Annual Security Report) 229 Days 100% is the median duration of how long breaches were present before discovery in 2013 (M-Trends Report)
  4. 4. 4 Bad guys know how to stay inside the bell curve. Why Is This So Hard? Unknown: Harder to detect • New behavior • Goes to an approved place • Works encrypted • Authorized Use • Inside of baseline • Outside monitored infrastructure • Matches a signature • Goes to a bad place • Works in the clear • Unauthorized Use • Outside of baseline • Within monitored infrastructure Known: Easier to detect
  5. 5. 5 The Geography Of Security Detection Has ChangedData flows in many ways – where should we catch and analyze it? Security Data Enterpris e Data Context Data Data Ocean Cyber Defense: Real-time correlation Known Attack Patterns Hunt Team: Long term analytics Unknown Attack Patterns Operational: Rivers of Data • SIEM and Platform protection • Attacks analyzed & responded to Tactical: Streams of Data • Endpoint protection & logs • Attacks easily detected / prevented Strategic: Oceans of Data • Often the missing piece • Contains important intelligence Endpoint and Network Security Signature & Pattern Based
  6. 6. 6 All Data Is Not Equal And expensive… • $collect, $process, $analyze, $store, $manage You should consider the small analytics problems first Collect what matters to solving a real problem – are all these logs useful? The conventional wisdom of collect everything and figure it out later is WRONG!
  7. 7. 7 Basic Context • Asset, Network • Identity Advanced Context • Application • Flow & DPI Technical Intelligence • Malware Detonation • IOC Identification Human Intelligence • Sentiment analysis • Motivation Adhoc Query • Small dataset • Basic analysis Advanced Search • Indicator lists • Pivot search Analytical Query • Big Data management • Analytical datamart Visualization • Exploratory data analysis Reporting • Threat • Compliance Scoring • Risk Fidelity • Profiling Data Mining • Clustering, Aggregation • Affinity Grouping Machine Learning • Classification • Other Algorithms Real-time • RT Correlation • Log Aggregation Historical Analysis • LT Correlation • Epidemiology Statistical Analysis • Distributed R • Standard deviation Behavioral • Insider Threat • Baselining Frontier Understand Explore Explain Detect Depth => Increase in Effectiveness Describing the Future of Security Detection Adding Advanced Analytics Existing Emerging Advanced Target
  8. 8. 8 What Stopped Us From This Kind Of Analysis?
  9. 9. 9 Analytics Of The Future Relies On Columnar Retrieval Compression Clustering Distributed Query
  10. 10. 10 Find Needles & Understand Haystacks Using… Classification - context (asset model, etc…) Correlation - real-time (ESM) & historical Clustering – common root cause Affinity Grouping - relationships in data Aggregation - assemble attacker profile Statistical Analysis – reporting & anomalies Disciplines of Analytics
  11. 11. 11 Visualization Of Big Data – Affinity Group Business Statement • Find command and control infrastructure in your enterprise Analytics Statement • Identify affinity groups • Investigate anomalous groupings 1 million events Anomalous Grouping Findings from Visualization • Hierarchical, highly-resilient C&C infrastructure This example reveals a command and control infrastructure
  12. 12. 12 Analyzing The Haystack - aka Reporting Time Volume
  13. 13. 13 Business Statement • Find sophisticated port scan activity (distributed, randomized) Analytics Statement • Plot multiple months of data on one scatterplot Billions of events Findings from Visualization • Single multi-week scan from distributed, internal sources indicates advanced attacker This example reveals a low and slow scan Visualization Of Big Data – Scatterplot
  14. 14. 14 Business Statement • Find servers talking to suspicious hosts outside the network Analytics Statement • Plot all suspicious successful communications and review Graph filtered from billions of events Findings from Visualization • A host communicated w/ suspicious external website • Unique in that no other host in the environment has ever talked to this external website This example reveals inappropriate communication (bottom 10 phenomenon) Anomalous Line Visualization Of Big Data – Anomaly Chart
  15. 15. 15 Exploratory Data Analysis Analytical Process • Select a question to answer • Identify the data that matters • Reduce the data to a manageable amount • Structure the problem (clean the data, categorize, normalize, articulate) • Conduct formal analysis (data mining, statistics, machine learning) • Conduct exploration / visualization (root cause analyze and remove) • Confirm findings and present results
  16. 16. 16 Hunt Team - The Way To Operationalize Analytics
  17. 17. 17 Operational Deception – Honeypot vs. Deception
  18. 18. 18 Analytical Talent: A Strong Fingerprint Exists Work in small teams – industry average 10 people Using tools more sophisticated than a spreadsheet is a qualifier Analytics personality? - Tom Davenport • Mindset: #1 intellectually curious more important than any specific skill • Desire to learn • Deep desire for creative assignments • Major in STEM and minor in liberal arts • Rigor and discipline are high • Important work matters to these folks
  19. 19. © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. They’re in there! Let’s find them.