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Deriving Risk Events through the Analysis of Distributed Netstat Data

Deriving Risk Events through the Analysis of Distributed Netstat Data. Timothy Wright Graduate Operating Systems Fall 2006. The Motivation. IT Risk Management (RM) Risk picture--accuracy depends on information Data Sources for Risk Events Network oriented Expense: keep it cheap

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Deriving Risk Events through the Analysis of Distributed Netstat Data

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  1. Deriving Risk Eventsthrough the Analysis ofDistributed Netstat Data Timothy Wright Graduate Operating Systems Fall 2006

  2. The Motivation • IT Risk Management (RM) • Risk picture--accuracy depends on information • Data Sources for Risk Events • Network oriented • Expense: keep it cheap • Resource needs: keep it simple

  3. The Problem What network data source is cheap and easy to obtain, yet provides great accuracy (e.g., when, what, where, how) and semantic (e.g., who, why) value?

  4. The Netstat Solution • Netstat • “Inside” perspective • Ubiquitous • Distributed Collection • Leverage both sides of a connection • Aggregate observations

  5. …but, the truly compelling thing about netstat is: All TCP/UDP activities can be tied to an account-level entity • User account associations • Program name associations (root only)

  6. Sample Netstat Output

  7. DragNet: Initial Architecture

  8. Data Drop Problem • Type 1: monitored host offline • Type 2: netstat-agent script failing to log • 83,184 connections logged; 1,371 Type 2 data drops detected (about 2%)

  9. Types of Reports Generated from Distributed Netstat Data • Traffic Composition • Various Kinds of Inbound/Outbound Connections • Human User Activities • Bipartite Matching

  10. Traffic Composition Report

  11. Bipartite Matching • Match up listeners with clients • Only works for connections among monitored hosts • Example non-matched connection: • Why would there be an SSH connection between cluster hosts on a Saturday afternoon? • Any risk here?

  12. Bipartite Matching (continued) Example matched connection: • Student account originated connection • User’s affiliation may diminish risk (e.g., in this case, activity seems legit)

  13. Conclusions and Future Work • Netstat data offer a strong information resource for IT RM • The “who” in network transactions • The “how” in network transactions (if root) • Heightened semantics • Issues with DragNet • Not real time (fixed) • Harvesting and processing netstat data (fixed) • Data drops • Type 1 (monitored via heartbeat) • Type 2 (mitigated)

  14. Questions and Comments

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