Northwestern Lab for Internet and Security Technology (LIST)

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

Northwestern Lab for Internet and Security

Technology (LIST)

Yan Chen

Department of Computer ScienceNorthwestern University

http://list.cs.northwestern.edu

• 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

Our Theme

Projects at LIST

• Global Router-based Anomaly/Intrusion Detection (GRAID) Systems

• Distributed Information Retrieval Systems

Battling Hackers is a Growth Industry!

• 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

--Wall Street Journal (11/10/2004)

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

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 Detection Sensor• Attached to a router or access point as a black

box• Edge network detection is particularly powerful

Router

LAN

Internet

Switch

LAN

(a)

Router

LAN

Internet

LAN

(b)

GRAID sensor

scan

po

rtsc

an

port

Splitter

Router

LAN

Internet

LAN

(c)

Splitter

GR

AID

sen

sor

Switch

Switch

Switch

Switch

Switch

GRAIDsensor

GRAIDsensor

Original configuration Monitor each port

separately

Monitor aggregated

traffic from all ports

GRAID Sensor

ArchitectureReversiblek-ary sketch monitoring

Filtering

Sketch based statistical anomaly detection (SSAD)

Local sketch records

Sent out for aggregation

Remote aggregatedsketchrecords

Per-flow monitoring

Streaming packet data

Normal flows

Suspicious flows

Intrusion or anomaly alarms to fusion centers

Keys of suspicious flows

Keys of normal flows

Data path Control pathModules on the critical path

Signature-based detection

Traffic profile checking

Statistical detection

Part ISketch-basedmonitoring & detection

Part IIPer-flowmonitoring & detection

Modules on the non-critical path

Network fault detection

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

Sketch-based Change Detection

(ACM SIGCOMM IMC 2003, 2004)

• Input stream: (key, update)

Sketchmodule

Forecastmodule(s)

Change detectionmodule

(k,u) … SketchesError

Sketch Alarms

• Report flows with large forecast errors

• Summarize input stream using sketches

• Build forecast models on top of sketches

Sketch• Probabilistic summary of data streams

– Originated in STOC 1996 [AMS96]– Widely used in database research to handle

massive data streams

Space Accuracy

Hash table Per-key state 100%

Sketch Compact With probabilistic guarantees (better for larger values)

K-ary Sketch

• Array of hash tables: Tj[K] (j = 1, …, H)

1

j

H

0 1 K-1…

……

hj(k)

hH(k)

h1(k)

• Update (k, u): Tj [ hj(k)] += u (for all j)

K

KsumkhT jjj /11

/)]([median

K-ary Sketch (cont’d)• Estimate v(S, k): sum of updates for key k

compensatefor signal loss

v(S, k) + noise

v(S, k)/K + E(noise)

boostconfidence

unbiased estimator of v(S,k) with low variance

1

j

H

0 1 K-1…

……

hj(k)

hH(k)

h1(k)

Forecast Model: EWMA•Sketches are linear (Can combine sketches)

•Compute forecast error sketch: Serror

=

Sforecast(t) Sobserved(t-1) Sforecast(t-1)

= -

Serror(t-1) Sobserved(t-1) Sforecast(t-1)

•Update forecast sketch: Sforecast

• 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

Evaluation of Reversible K-ary Sketch

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

GRAID Sensor


Filtering





Per-flow monitoring


Normal flows

Suspicious flows












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 scanningMonitor 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

Intrusion Mitigation

Attacks detected MitigationDenial of Service (DoS), e.g., TCP SYN flooding

SYN defender, SYN proxy, or SYN cookie for victim

Port Scan and worms Ingress filtering with attacker IPVertical port scan Quarantine the victim machineHorizontal port scan Monitor traffic with the same

port # for compromised machine

Spywares Warn the end users being spied

HORIZONTAL

PORT NUMBER

SOURCE IP

BLOCK

VERTICAL

GRAID Sensor


Filtering





Per-flow monitoring


Normal flows

Suspicious flows












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

1 2

1’1

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)

GRAID Sensor


Filtering





Per-flow monitoring


Normal flows

Suspicious flows












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

GRAID Sensor Interconnection

• Though Cyber Disease DHT (distributed hash table) for alarm fusion– Scalability– Load balancing– Fault-tolerance– Intrusion

correlation

Internet

IDSIDS + SFC

GRAID Coverage

AttackInjected

AttackInjected

CDDHTMesh

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

Intrusion ID Characterization Field(s)

DoS Attack 0 Victim IP (subnet)

Scans 1 0 (for vertical & block scan)

Source IP address

Destination IP (for vertical scan)

0 (for block scan)

1 (for horizontal & coordinated scan)

Scan port number

Source IP (for horizontal scan)

0 (for coordinated scan)

Viruses/Worms 2 0 (for known virus/worm) Worm ID

1 (for unknown virus/worm) Destination port number

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?

Research methodologyCombination of theory, synthetic/real trace

driven simulation, and real-world implementation and deployment

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)

Documents

Northwestern Lab for Internet and Security Technology (LIST)