Network Intrusion Detection and Mitigation

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

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

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

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

The Spread of Sapphire/Slammer Worms

How can it affect cell phones? • Cabir worm can infect a cell phone • Infect phones running Symbian OS • Started in Philippines at the end of 2004, surfaced in Asia, Latin America, Europe, and recently in US • Posing as a security management utility • Once infected, propagate itself to other phones via Bluetooth wireless connections • Symbian officials said security was a high priority of the latest software, Symbian OS Version 9. • With ubiquitous Internet connections, more severe viruses/worms for mobile devices will happen soon …

Cable Modem Premises- based AccessNetworks LAN Transit Net LAN LAN Private Peering Premises- based Core Networks Transit Net WLAN WLAN NAP Analog WLAN Transit Net Public Peering DSLAM Operator- based RAS Regional Wireline Regional Cell H.323 Data Cell Data H.323 Cell PSTN Voice Voice The Current Internet: Connectivity and Processing

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

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

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

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

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

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

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) • Each access point (AP) can have 200 Mbps – a collection of 10-100 APs can easily go up to 2-20 Gbps • The Moore’s Law on traffic growth …  • Per-flow analysis is too slow or too expensive • Want to work in near real time

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

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

Current IDS Insufficient for Wireless Networks • Most existing IDS signature-based • Especially for wireless networks • Detect denial-of-service attacks caused by the WEP authentication vulnerability, e.g., Airespace • Current statistical IDS has manually set parameters • Cannot adapt to the traffic pattern changes • However, wireless networks often have transient connections • Hard to differentiate collisions, interference, and attacks

Statistical Anomaly/Intrusion Detection and Mitigation for Wireless Networks • Use statistics from MIB of AP to understand the current wireless network status • Metrics considered: capacity, transmission fail count, multiple retry count, duplicate count, received fragment count, etc. • Infer the wireless network status: congested ? Interfered ? • Automatically adapt to different learned profiles on observing status changes • Applicable to both WLAN and celluar network infrastructure protection

Intrusion Detection and Mitigation

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 SIGCOMM04 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

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

Potential Collaborative Research Areas with Motorola • Wireless virus/worm detection • Spyware detection • Both by operators at infrastructure level (e.g., access point) • Intrusion detection and mitigation for cellular network infrastructure • Automatic attack responding and survival for Motorola infrastructure products

Thank You! More Questions?

Backup Slides

RF Management and Monitoring (e.g., Airespace) • Rogue Access Point/Ad-Hoc networks • RF Interference • Fake Access Point • AP Impersonation • Spoofed Deauthenticate Frame • Honeypot AP

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

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)

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

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

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

CDDHT: Disease Key Design

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?

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)

Network Intrusion Detection and Mitigation