1 Network-based Intrusion Detection, Prevention and Forensics System Yan Chen Department of Electrical Engineering and Computer Science Northwestern University Lab for Internet & Security Technology (LIST)

2 The Spread of Sapphire/Slammer Worms

3 Current Intrusion Detection Systems (IDS) Mostly host-based and not scalable to high- speed networks –Slammer worm infected 75,000 machines in <10 mins –Host-based schemes inefficient and user dependent »Have to install IDS on all user machines ! Mostly simple signature-based –Cannot recognize unknown anomalies/intrusions –New viruses/worms, polymorphism

4 Current Intrusion Detection Systems (II) Cannot provide quality info for forensics or situational-aware analysis –Hard to differentiate malicious events with unintentional anomalies »Anomalies can be caused by network element faults, e.g., router misconfiguration, link failures, etc., or application (such as P2P) misconfiguration –Cannot tell the situational-aware info: attack scope/target/strategy, attacker (botnet) size, etc.

5 Network-based Intrusion Detection, Prevention, and Forensics System Online traffic recording [SIGCOMM IMC 2004, INFOCOM 2006, ToN to appear] –Reversible sketch for data streaming computation –Record millions of flows (GB traffic) in a few hundred KB –Small # of memory access per packet –Scalable to large key space size (2 32 or 2 64 ) Online sketch-based flow-level anomaly detection [IEEE ICDCS 2006] [IEEE CG&A, Security Visualization 06] –Adaptively learn the traffic pattern changes –As a first step, detect TCP SYN flooding, horizontal and vertical scans even when mixed Online stealthy spreader (botnet scan) detection [IWQoS 2007]

6 Network-based Intrusion Detection, Prevention, and Forensics System (II) Polymorphic worm signature generation & detection [IEEE Symposium on Security and Privacy 2006] [IEEE ICNP 2007 to appear] Accurate network diagnostics [ACM SIGCOMM 2006] [IEEE INFOCOM 2007] Scalable distributed intrusion alert fusion w/ DHT [SIGCOMM Workshop on Large Scale Attack Defense 2006] Large-scale botnet and P2P misconfiguration event forensics [work in progress]

7 System Deployment Attached to a router/switch as a black box Edge network detection particularly powerful Original configuration Monitor each port separately Monitor aggregated traffic from all ports Router LAN Inter net Switch LAN (a) Router LAN Inter net LAN (b) RAND system scan port Splitter Router LAN Inter net LAN (c) Splitter RAND system Switch HPNAIDM system RAND system

Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience Zhichun Li, Manan Sanghi, Yan Chen, Ming-Yang Kao and Brian Chavez Northwestern University

Desired Requirements for Polymorphic Worm Signature Generation Network-based signature generation –Worms spread in exponential speed, to detect them in their early stage is very crucial… However »At their early stage there are limited worm samples. –The high speed network router may see more worm samples… But »Need to keep up with the network speed ! »Only can use network level information

Desired Requirements for Polymorphic Worm Signature Generation No existing work satisfies these requirements ! Noise tolerant –Most network flow classifiers suffer false positives. –Even host based approaches can be injected with noise. Attack resilience –Attackers always try to evade the detection systems Efficient signature matching for high-speed links

Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

Choice of Signatures Two classes of signatures –Content based »Token: a substring with reasonable coverage to the suspicious traffic »Signatures: conjunction of tokens –Behavior/vulnerability based Our choice: content based –Fast signature matching. ASIC based approach can archive 6 ~ 8Gb/s –Generic, independent of any protocol or server

Unique Invariants of Worms Protocol Frame –The code path to the vulnerability part, usually infrequently used –Code-Red II: ‘.ida?’ or ‘.idq?’ Control Data: leading to control flow hijacking –Hard coded value to overwrite a jump target or a function call Worm Executable Payload –CLET polymorphic engine: ‘0\x8b’, ‘\xff\xff\xff’ and ‘t\x07\xeb’ Possible to have worms with no such invariants, but very hard Invariants

Hamsa Architecture

Hamsa Design Key idea: model the uniqueness of worm invariants –Greedy algorithm for finding token conjunction signatures Highly accurate while much faster –Both analytically and experimentally –Compared with the latest work, polygraph –Suffix array based token extraction Provable attack resilience guarantee Noise tolerant

Hamsa Signature Generator Core part: Model-based Greedy Signature Generation Iterative approach for multiple worms

Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

Problem Formulation Signature Generator Signature false positive bound  Maximize the coverage in the suspicious pool False positive in the normal pool is bounded by  Suspicious pool Normal pool With noise NP-Hard!

Model Uniqueness of Invariants FP 21% 9% 17% 5% t1t1 Joint FP with t 1 2% 0.5% 1% t2t2 The total number of tokens bounded by k* U(1)=upper bound of FP( t 1 ) U(2)=upper bound of FP( t 1,t 2 )

Signature Generation Algorithm (82%, 50%) (COV, FP) (70%, 11%) (67%, 30%) (62%, 15%) (50%, 25%) (41%, 55%) (36%, 41%) (12%, 9%) u(1)=15% Suspicious pool tokens token extraction Order by coverage t1t1

(82%, 50%) (COV, FP) (70%, 11%) (67%, 30%) (62%, 15%) (50%, 25%) (41%, 55%) (36%, 41%) (12%, 9%) t1t1 Order by joint coverage with t 1 (69%, 9.8%) (COV, FP) (68%, 8.5%) (67%, 1%) (40%, 2.5%) (35%, 12%) (31%, 9%) (10%, 0.5%) u(2)=7.5% t2t2 Signature Signature Generation Algorithm

Algorithm Analysis Runtime analysis O(T*(|M|+|N|)) Provable Attack Resilience Guarantee –Analytically bound the worst attackers can do! –Example: K*=5, u(1)=0.2, u(2)=0.08, u(3)=0.04, u(4)=0.02, u(5)=0.01 and  =0.01 –The better the flow classifier, the lower are the false negatives Noise ratioFP upper boundFN upper bound 5%1%1.84% 10%1%3.89% 20%1%8.75%

Attack Resilience Assumptions Two common assumptions for any sig generation sys Two unique assumptions for token-based schemes Attacks to the flow classifier –Our approach does not depend on perfect flow classifiers –With 99% noise, no approach can work! –High noise injection makes the worm propagate less efficiently. Enhance flow classifiers

Improvements to the Basic Approach Generalizing Signature Generation –use scoring function to evaluate the goodness of signature Iteratively use single worm detector to detect multiple worms –At the first iteration, the algorithm find the signature for the most popular worms in the suspicious pool. –All other worms and normal traffic treat as noise.

Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

Experiment Methodology Experiential setup: –Suspicious pool: »Three pseudo polymorphic worms based on real exploits (Code-Red II, Apache-Knacker and ATPhttpd), »Two polymorphic engines from Internet (CLET and TAPiON). –Normal pool: 2 hour departmental http trace (326MB) Signature evaluation: –False negative: 5000 generated worm samples per worm –False positive: »4-day departmental http trace (12.6 GB) »3.7GB web crawling including.mp3,.rm,.ppt,.pdf,.swf etc. »/usr/bin of Linux Fedora Core 4

Results on Signature Quality Single worm with noise –Suspicious pool size: 100 and 200 samples –Noise ratio: 0%, 10%, 30%, 50%, 70% –Noise samples randomly picked from the normal pool –Always get above signatures and accuracy. Multiple worms with noises give similar results Worms Training FN Training FP Evaluation FN Evaluation FP Binary evaluation FP Signature Code-Red II {'.ida?': 1, '%u780': 1, ' HTTP/1.0\r\n': 1, 'GET /': 1, '%u': 2} CLET00.109% %0.268% {'0\x8b': 1, '\xff\xff\xff': 1,'t\x07\xeb': 1}

Speed Results Implementation with C++/Python –500 samples with 20% noise, 100MB normal traffic pool, 15 seconds on an XEON 2.8Ghz, 112MB memory consumption Speed comparison with Polygraph –Asymptotic runtime: O(T) vs. O(|M| 2 ), when |M| increase, T won’t increase as fast as |M|! –Experimental: 64 to 361 times faster (polygraph vs. ours, both in python)

Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

Related works HamsaPolygraphCFGPADSNemeanCOVERSMalware Detection Network or host based Network Host Content or behavior based Content based Behavior based Content based Behavior based Noise tolerance YesYes (slow) YesNo Yes Multi worms in one protocol YesYes (slow) YesNoYes On-line sig matching Fast SlowFast Slow GeneralityGeneral purpose Protocol specific Server specific General purpose Provable atk resilience YesNo Information exploited   

Conclusion Network based signature generation and matching are important and challenging Hamsa: automated signature generation –Fast –Noise tolerant –Provable attack resilience –Capable of detecting multiple worms in a single application protocol Proposed a model to describe the worm invariants

Outline Overview of Network Intrusion Detection, Prevention and Forensics System Case Study: Polymorphic Worm Signature Generation Student recruiting

Northwestern Lab for Internet and Security Technology (LIST) About Northwestern Univ. – US News and World Report, overall ranking #14, the Engineering grad school ranking #21. – On the Michigan lake, close to Chicago downtown Sponsors for LIST: – Department of Energy (Early CAREER Award) – Air Force Office of Scientific Research (Young Investigator Award) – National Science Foundation – Microsoft Research – Motorola Inc.

34 Recruiting Ph.D. Students Bachelor in Computer Science or Computer Engineering Research experience a big plus TOEFL GRE Strongly motivated in independent research Feel free to talk to me after the talk