Northwestern Lab for Internet and Security Technology (LIST)

1. Northwestern Lab for Internet and Security Technology (LIST) Yan Chen • Router-based Anomaly/Intrusion Detection and Mitigation (RAIDM) Systems • Scalable and Accurate Overlay Network Monitoring and Diagnosis • Wireless and Ad hoc Networking

2. Northwestern Lab for Internet and Security Technology (LIST) Yan Chen Department of Computer Science Northwestern University http://list.cs.northwestern.edu

3. Our Theme • Internet is becoming a new infrastructure for service delivery • World wide web, • VoIP • Email • Interactive TV? • Major challenges for Internet-scale services • Scalability: 600M users, 35M Web sites, 2.1Tb/s • Security: viruses, worms, Trojan horses, etc. • Mobility: ubiquitous devices in phones, shoes, etc. • Agility: dynamic systems/network, congestions/failures • Ossification: extremely hard to deploy new technology in the core

4. Projects at LIST • Global Router-based Anomaly/Intrusion Detection (GRAID) Systems • Distributed Information Retrieval Systems

5. Battling Hackers is a Growth Industry! --Wall Street Journal (11/10/2004) • The past decade has seen an explosion in the concern for the security of information • Internet attacks are increasing in frequency, severity and sophistication • Denial of service (DoS) attacks • Cost $1.2 billion in 2000 • Thousands of attacks per week in 2001 • Yahoo, Amazon, eBay, Microsoft, White House, etc., attacked

6. Battling Hackers is a Growth Industry (cont’d) • Virus and worms faster and powerful • Melissa, Nimda, Code Red, Code Red II, Slammer … • Cause over $28 billion in economic losses in 2003, growing to over $75 billion in economic losses by 2007. • Code Red (2001): 13 hours infected >360K machines - $2.4 billion loss • Slammer (2003): 10 minutes infected > 75K machines - $1 billion loss • Spywares are ubiquitous • 80% of Internet computers have spywares installed

7. The Spread of Sapphire/Slammer Worms

8. 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 signature-based • Cannot recognize unknown anomalies/intrusions • New viruses/worms, polymorphism • Statistical detection • Hard to adapt to traffic pattern changes • Unscalable for flow-level detection • IDS vulnerable to DoS attacks • Overall traffic based: inaccurate, high false positives

9. Current Intrusion Detection Systems (II) • Cannot differentiate malicious events with unintentional anomalies • Anomalies can be caused by network element faults • E.g., router misconfiguration, signal interference of wireless network, etc. • Isolated or centralized systems • Insufficient info for causes, patterns and prevalence of global-scale attacks

10. Global Router-based Anomaly/Intrusion Detection (GRAID) Systems • Online traffic recording and analysis for high-speed networks • Leverage sketches for data streaming computation • Online adaptive flow-level anomaly/intrusion detection and mitigation • Leverage statistical learning theory (SLT) adaptively learn the traffic pattern changes • E.g., busy vs. idle wireless networks, with different level of interferences, etc. • Unsupervised learning without knowing ground truth

11. GRAID Systems (II) • Integrated approach for false positive reduction • Signature-based detection • Network element fault diagnostics • Traffic signature matching of emerging applications • Hardware speedup for real-time detection • Collaborated with Gokhan Memik (ECE of NU) • Try various hardware platforms: FPGAs, network processors • Scalable anomaly/intrusion alarm fusion with distributed hash tables (DHT) • Automatically distribute alerts with similar symptoms to the same fusion center for analysis

12. GRAID sensor GRAID sensor Internet scan port Internet LAN Internet LAN GRAID sensor LAN Switch Switch Splitter Switch Splitter Router Router Switch Switch Router scan port LAN LAN Switch LAN (a) GRAID sensor (b) (c) GRAID Detection Sensor • Attached to a router or access point as a black box • Edge network detection is particularly powerful Monitor each port separately Monitor aggregated traffic from all ports Original configuration

13. Remote aggregated sketch records Sent out for aggregation GRAID Sensor Architecture Part I Sketch-based monitoring & detection Reversible k-ary sketch monitoring Normal flows Sketch based statistical anomaly detection (SSAD) Local sketch records Streaming packet data Keys of suspicious flows Filtering Keys of normal flows Statistical detection Signature-based detection Per-flow monitoring Network fault detection Part II Per-flow monitoring & detection Suspicious flows Traffic profile checking Intrusion or anomaly alarms to fusion centers Modules on the critical path Modules on the non-critical path Data path Control path

14. Scalable Traffic Monitoring and Analysis - Challenge • Potentially tens of millions of time series ! • Need to work at very low aggregation level (e.g., IP level) • Changes may be buried inside aggregated traffic • The Moore’s Law on traffic growth …  • Per-flow analysis is too slow or too expensive • Want to work in near real time • Existing approaches not directly applicable • Mostly focus on heavy-hitters

15. ErrorSketch Sketchmodule Forecastmodule(s) Change detectionmodule (k,u) … Alarms Sketches Sketch-based Change Detection(ACM SIGCOMM IMC 2003, 2004) • Input stream: (key, update) • Summarize input stream using sketches • Build forecast models on top of sketches • Report flows with large forecast errors

16. Sketch • Probabilistic summary of data streams • Originated in STOC 1996 [AMS96] • Widely used in database research to handle massive data streams

17. … h1(k) 0 1 K-1 1 … hj(k) j … hH(k) H K-ary Sketch • Array of hash tables: Tj[K] (j = 1, …, H) • Update (k, u): Tj [ hj(k)] += u (for all j)

18. unbiased estimator of v(S,k) with low variance boostconfidence compensatefor signal loss v(S, k) + noise v(S, k)/K + E(noise) … h1(k) 0 1 K-1 1 … hj(k) j hH(k) … H K-ary Sketch (cont’d) • Estimate v(S, k): sum of updates for key k

19. =- Serror(t-1) Sforecast(t) Sobserved(t-1) Sobserved(t-1) Sforecast(t-1) Sforecast(t-1) = *a+*(1-a) Forecast Model: EWMA • Sketches are linear (Can combine sketches) • Compute forecast error sketch: Serror • Update forecast sketch:Sforecast

20. Evaluation of Reversible K-ary Sketch • Evaluated with tier-1 ISP trace and NU traces • Scalable • Can handle tens of millions of time series • Accurate • Provable probabilistic accuracy guarantees • Even more accurate on real Internet traces • Efficient • For the worst case traffic, all 40 byte packets: • 16 Gbps on a single FPGA board • 526 Mbps on a Pentium-IV 2.4GHz PC • Only less than 3MB memory used • Patent filed

21. Remaining Challenges • Reversible sketch to infer the culprit flows (ACM SIGCOMM IMC 2004) • Hierarchical and multi-dimensional sketch • Detecting distributed and insidious attacks with sketch

22. Remote aggregated sketch records Sent out for aggregation GRAID Sensor Architecture Part I Sketch-based monitoring & detection Reversible k-ary sketch monitoring Normal flows Sketch based statistical anomaly detection (SSAD) Local sketch records Streaming packet data Keys of suspicious flows Filtering Keys of normal flows Statistical detection Signature-based detection Per-flow monitoring Network fault detection Part II Per-flow monitoring & detection Suspicious flows Traffic profile checking Intrusion or anomaly alarms to fusion centers Modules on the critical path Modules on the non-critical path Data path Control path

23. Statistical Anomaly Detection • Online statistical detection with sketches • Applying Statistical Learning Theory (STL) • Use Hidden Markov Model (HMM) to adaptively learn the parameters • Focus on two major intrusions: denial of service (DoS) attacks and port scanning Monitor traffic with multiple sketches • With different keys • (Source IP, Dest IP) • (Source IP, Dest port) • (Dest IP, Dest port) • For each key, record the number of unconnected TCP requests: SYN – SYN/ACK

24. Intrusion Mitigation

25. Remote aggregated sketch records Sent out for aggregation GRAID Sensor Architecture Part I Sketch-based monitoring & detection Reversible k-ary sketch monitoring Normal flows Sketch based statistical anomaly detection (SSAD) Local sketch records Streaming packet data Keys of suspicious flows Filtering Keys of normal flows Statistical detection Signature-based detection Per-flow monitoring Network fault detection Part II Per-flow monitoring & detection Suspicious flows Traffic profile checking Intrusion or anomaly alarms to fusion centers Modules on the critical path Modules on the non-critical path Data path Control path

26. 1’ 1 1 2 Network Diagnosis and Fault Location • Infrastructure ossification led to thrust of overlay applications • Traceroute gives hop-by-hop round-trip latency • Asymmetric routing • Can’t get hop-by-hop loss rate ! • Network tomography • Infer the properties of links from end-to-end measurements • Limited measurements -> under-constrained system, unidentifiable links • Existing work uses various constraints and assumptions • Tree-like topology • The number of lossy links is small

27. Our Approach: Virtual Links • Minimal link sequences (path segments) whose loss rates uniquely identified • Locate the faults to certain link(s) • The first lower-bound on the network tomography granularity • Use algebraic scheme to find virtual links • Leverage our work on overlay network monitoring (ACM SIGCOMM IMC 2003, ACM SIGCOMM 2004)

28. Remote aggregated sketch records Sent out for aggregation GRAID Sensor Architecture Part I Sketch-based monitoring & detection Reversible k-ary sketch monitoring Normal flows Sketch based statistical anomaly detection (SSAD) Local sketch records Streaming packet data Keys of suspicious flows Filtering Keys of normal flows Statistical detection Signature-based detection Per-flow monitoring Network fault detection Part II Per-flow monitoring & detection Suspicious flows Traffic profile checking Intrusion or anomaly alarms to fusion centers Modules on the critical path Modules on the non-critical path Data path Control path

29. Intrusion/anomaly Alarm Fusion • Individual IDS has bad accuracy due to limited view • Crucial to collect information from multiple vantage points – distributed IDS (DIDS) • Each IDS generate local symptom report, send to sensor fusion center (SFC) • Help understand the prevalence, cause and patterns of global-scale attacks • Existing DIDS • Centralized fusion • Distributed fusion with unscalable communication

30. Attack Injected GRAID Coverage Internet IDS CDDHT Mesh IDS + SFC Attack Injected GRAID Sensor Interconnection • Though Cyber Disease DHT (distributed hash table) for alarm fusion • Scalability • Load balancing • Fault-tolerance • Intrusion correlation

31. Basic Operations of CDDHT • put (disease_key, symptom report) • Send report to SFC • attack_info = get (disease_key) • Query about certain attacks from SFC • Each operation only O(n) hops • n is the total number of nodes in CDDHT

32. CDDHT: Disease Key Design

33. Other Challenges of CDDHT • Load balancing • Supporting complicated queries • E.g., aggregate queries • Attack resilience • OK to have some IDS sensors compromised • What about SFCs?

34. Research methodology Combination of theory, synthetic/real trace driven simulation, and real-world implementation and deployment

35. Conclusion for GRAID Systems • Online traffic recording and analysis on high-speed networks • Online statistical anomaly detection • Integrated approach for false positive reduction • Signature-based detection • Network element fault diagnostics • Traffic signature matching of emerging applications • Hardware speedup for real-time detection • Scalable anomaly/intrusion alarm fusion with distributed hash tables (DHT)