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Survey of Intrusion Detection Systems

Survey of Intrusion Detection Systems. Motivation. The worldwide impact of malicious code attacks is estimated to be over $10 Billion annually.

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Survey of Intrusion Detection Systems

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  1. Survey of Intrusion DetectionSystems

  2. Motivation • The worldwide impact of malicious code attacks is estimated to be over $10 Billion annually. • The CERT center at CMU reported 73,359 security incidents between 1/1/02 and 9/31/02, equal to all of the security incidents reported in 2000-2001 combined. • Novice attackers can easily acquire and use automated denial-of-service attack software. • Human security analysts can't keep up with it all

  3. Intrusion Detection Attempts to detect unauthorized or malicious activities in a network or on a host system • Signature-based - looks for patterns that are known to be intrusive in packets or audit logs • Anomaly-based - looks for 'abnormal' activity, usually requires a template of 'normal' activity Determining 'who' is much harder than just detecting that an intrusion occurred.

  4. Early Work on Security • Saltzer and Schroeder (1974) - established security design principals and mechanisms • Orange Book (1985) - DoD specifications • Formal Models • Bell -LaPadula (1976) - supported formal proofs of conformance to security policies • Denning (1987) - described the requirements for designing an intrusion detection system

  5. Early Systems • IDES - statistical anomaly detection • Haystack - also added signature detection • Wisdom & Sense - automatically created a profile of 'normal' behavior from past user and host activities • ISOA - uses both real-time monitoring and post-session analysis to detect suspicious behavior, developed profiles at both levels

  6. Recent Research in ID • NIDES - distributed collection of host data, centralized analysis (extension of IDES) • NSM - network traffic monitoring for anomalous packets • DIDS - combines host-based (Haystack) and network monitoring (NSM) • CSM - peer-to-peer distributed analysis

  7. Recent Research (continued) • Bro - analyzes packet contents • GrIDS - builds graphs of network activity and looks for anomalies • STAT and NetSTAT - model attack with state machine. if accepted, attack occurred • EMERALD - framework for building an ID system with distributed collection and analysis, modular design (extended NIDES)

  8. Additional IDS Projects • Data-mining for ID - numerous projects mining host audit data, captured packets • Autonomous Agents - independent agents monitor specific activities/resources and report to hierarchy of analyzers • Open source projects - (e.g. SHADOW and Snort) - performance comparable to commercial and research systems

  9. Major Problems • High False-Alarm Rates - real-world tests show overwhelming numbers of false alarms, little success in filtering them out • Availability of Training Data - most anomaly-based ID systems need attack-free datasets. Currently, no clear way to create or certify realistic attack-free data

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