towards high performance network defense zhichun li eecs department northwestern university

Towards High Performance Network Defense

Zhichun LiEECS Department

Northwestern University

2

Motivation

Botnets

Worms

Attackers

Professional attackers exploit networks for profit $$$

3

Network Level Defense

• Network gateways/routers are the vantage points for detecting large scale attacks

• Only host based detection/prevention is not enough– Some users do not apply the host-based schemes

due to the reliability, overhead, and conflicts– Many users do not update or patch their system on

time – E.g., Conficker worm in the end of 2008 infected 9~15

millions of hosts– Cannot only reply on end users for security protection

4

Challenges

• Scalable to high speed networks with a large number of users

• Highly accurate

• Adapt fast to the emerging threats

• Have good attack coverage

5

Network-based Intrusion Detection, Prevention, and Forensics System

• Framework(I) Sketch based monitoring & detection

(III) Signature matching engines

(II) Polymorphic worm signature generation

(IV) Network situational awareness

Packetstreams

Accuracy &adapt fast


Scalability

Accuracy &Scalability & Coverage

6

High-speed Network Monitoringand Anomaly Detection

• Online traffic monitoring and recording [SIGCOMM IMC 2004, INFOCOM 2006, ToN 2007] [INFOCOM 2008]

– 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 (232 or 264)

• Online sketch-based flow-level anomaly detection [IEEE ICDCS 2006] [Journal of Computer Networks 2010] [IEEE CG&A, Security Visualization 2006]

• Online stealthy botnet scan detection [IEEE IWQoS 2007]

1

j

H

0 1 K-1…

……

hj(k)

hH(k)

h1(k)

7

Network and Distributed System Diagnosis

• Overlay network monitoring and diagnosis [SIGCOMM IMC 2003, SIGCOMM 2004, ToN 2007] [SIGCOMM 2006]

• End-user network diagnosis [INFOCOM 2007 (2)]

• Internet-scale Virtual Private Network (VPN) and backbone monitoring and diagnosis [INFOCOM 2009]

• Internet-scale Data Center and dist system profiling and diagnosis [NSDI 2010]

88

• Exploit invariant signature generation [IEEE Symposium on Security and Privacy 2006] (cited by ~100, code and test cases release to Columbia U., UT Austin, Purdue, Georgia Tech, UC Davis, etc)

• Vulnerability signature generation [IEEE ICNP 2007, ToN 2010]

[NSF CyberTrust 06 Award]1010101

10111101

11111100

00010111

Network gatewayInternet

Polymorphic Worm Signature Generation

Our network

99

• NetShield vulnerability signature based NIDS/NIPS [NSF CyberTrust 08 Award] [under submission] [patent filed]– Interested by Cisco (IPS ruleset & site visit)– Code release has been used by researchers in

University of Toronto

• Using failure information to detect enterprise zombies [SecureCom09]

• Spamming botnet detection [NSDI09]

Online Protocol Parsing and Signature Matching

1010

• Large-scale botnet and P2P misconfiguration event situational-aware forensics– Botnet attack target/strategy inference [ASIACCS09] – Root cause analysis of the P2P

misconfiguration/poisoning traffic [INFOCOM10]• Analysis of 2TB data across 4 years over 5 /8 IPs

Network Situational Awareness

Peers

File Request Flooding

Innocent VictimMisconfigured Traffic

DDoS attack Scenario

Current Work

• Data center management and configuration

• Internet emergency response– AS topology study [CoNEXT09]– Recovery via IXP [Infocom10]

• Network based web dynamic vulnerability defense

• Social network security

11

12

NetShield: Matching a Large Vulnerability Signature Ruleset for High Performance Network

Defense

1313

Outline

• Motivation

• High Speed Matching for Large Rulesets

• High Speed Parsing

• Evaluation

• Research Contributions

14

NetShield Overview NIDS/NIPS (Network Intrusion

Detection/Prevention System) operation

Signature DB

NIDS/NIPS `

`

`

Packets

Securityalerts

• Accuracy• Speed• Attack Coverage

15

State Of The Art

Pros• Can efficiently match multiple sigs simultaneously,

through DFA• Can describe the syntactic context

Regular expression (regex) based approachesUsed by: Cisco IPS, Juniper IPS, open source Bro

Example: .*Abc.*\x90+de[^\r\n]{30}

Cons of Regex

Regex ContextFree

ContextSensitive

Protocol grammar

Theoretical prospective

Practical prospective

• HTTP chunk encoding

• DNS label pointers

Limited expressive power, cannot describe semantic context, thus inaccurate

17

State Of The Art

Pros• Directly describe

semantic context• Very expressive, can

express the vulnerability condition exactly

• Accurate

Vulnerability Signature [Wang et al. 04]

Cons• Slow! • Existing approaches all

use sequential matching• Require protocol parsing

Blaster Worm (WINRPC) Example:BIND:rpc_vers==5 && rpc_vers_minor==1 && packed_drep==\x10\x00\x00\x00&& context[0].abstract_syntax.uuid=UUID_RemoteActivationBIND-ACK:rpc_vers==5 && rpc_vers_minor==1CALL:rpc_vers==5 && rpc_vers_minors==1 && packed_drep==\x10\x00\x00\x00&& opnum==0x00 && stub.RemoteActivationBody.actual_length>=40&& matchRE(stub.buffer, /^\x5c\x00\x5c\x00/)

Goodstate

BadstateVulnerability

Signature

Vulnerability: design flaws enable the bad inputs lead the program to a bad state

Bad input

18

Motivation of NetShield

18

Theoretical accuracy limitation of regex

State of the art regex Sig

IDSesNetShield

Existing Vulnerability

Sig IDS

Accuracy HighLow

Low

Hig

hS

peed

1919

Motivation• Desired Features for Signature-based

NIDS/NIPS– Accuracy (especially for IPS)– Speed– Coverage: Large ruleset

Regular Expression

Vulnerability

Accuracy Relative Poor

Much Better

Speed Good ??

Memory OK ??

Coverage Good ??

Shield[sigcomm’04]

Focus of this work

Cannot capture vulnerability condition well!

2020

Research Challenges and Solutions

• Challenges– Matching thousands of vulnerability

signatures simultaneously• Sequential matching match multiple sigs.

simultaneously

– High speed protocol parsing

• Solutions– An efficient algorithm which matches multiple

sigs simultaneously– A tailored parsing design for high-speed

signature matching

2121

Background

• Vulnerability signature basic– Use protocol semantics to express vulnerabilities– Defined on a sequence of PDUs & one predicate for

each PDU– Example: ver==1 && method==“put” && len(buf)>300

• Data representations– For all the vulnerability signatures we studied, we only

need numbers and strings– number operators: ==, >, <, >=, <=– String operators: ==, match_re(.,.), len(.).

Blaster Worm (WINRPC) Example:BIND:rpc_vers==5 && rpc_vers_minor==1 && packed_drep==\x10\x00\x00\x00&& context[0].abstract_syntax.uuid=UUID_RemoteActivationBIND-ACK:rpc_vers==5 && rpc_vers_minor==1CALL:rpc_vers==5 && rpc_vers_minors==1 && packed_drep==\x10\x00\x00\x00&& opnum==0x00 && stub.RemoteActivationBody.actual_length>=40 && matchRE(stub.buffer, /^\x5c\x00\x5c\x00/)

2222

Outline

• Motivation

• High Speed Matching for Large Rulesets


• Evaluation


23

Matching Problem Formulation• Suppose we have n signatures, defined on k

matching dimensions (matchers)– A matcher is a two-tuple (field, operation) or a four-

tuple for the associative array elements– Translate the n signatures to a n by k table– This translation unlocks the potential of matching

multiple signatures simultaneously

Rule 4: URI.Filename=“fp40reg.dll” && len(Headers[“host”])>300RuleID Method == Filename == Header == LEN

1 DELETE * *

2 POST Header.php *

3 * awstats.pl *

4 * fp40reg.dll name==“host”; len(value)>300

5 * * name==“User-Agent”; len(value)>544

2424

Matching Problem Formulation

• Challenges for Single PDU matching problem (SPM)– Large number of signatures n– Large number of matchers k– Large number of “don’t cares”– Cannot reorder matchers arbitrarily --

buffering constraint– Field dependency

• Arrays, associative arrays• Mutually exclusive fields.

25

Difficulty of the SPM

• Bad News– A well-known computational geometric problem

can be reduced to this problem. – And that problem has bad worst case bound

O((log N)K-1) time or O(NK) space (worst case ruleset)

• Good News– Measurement study on Snort and Cisco ruleset– The real-world rulesets are good: the

matchers are selective.– With our design O(K)

2626

Matching Algorithms

Candidate Selection Algorithm

1.Pre-computation decides the rule order and matcher order

2.Decomposition. Match each matcher separately and iteratively combine the results efficiently

• Integer range checking balanced binary search tree

• String exact matching Trie• Regex DFA (XFA)

27

Step 1: Pre-Computation• Optimize the matcher order based on buffering

constraint & field arrival order • Rule reorder:

RequireMatcher 1

Don’t careMatcher 1

RequireMatcher 1

RequireMatcher 2

Don’t careMatcher 1

& 2

1

n

2828

Step 2: Iterative Matching

RuleID Method == Filename == Header == LEN

1 DELETE * *

2 POST Header.php *

3 * awstats.pl *

4 * fp40reg.dll name==“host”; len(value)>300

5 * * name==“User-Agent”; len(value)>544

PDU={Method=POST, Filename=fp40reg.dll, Header: name=“host”, len(value)=450}

S1={2} Candidates after match Column 1 (method==)S2= S1 A2+B2={2} {}+{4}={}+{4}={4}S3=S2 A3+B3 ={4} {4}+{}={4}+{}={4}

1 ii AS

Si1 ii AS

Don’t care matcher i+1

requirematcher i+1

In Ai+1

R1

R2

R3

29

Complexity Analysis

• Merging complexity– Need k-1 merging iterations– For each iteration

• Merge complexity O(n) the worst case, since Si can have O(n) candidates in the worst case rulesets

• For real-world rulesets, # of candidates is a small constant. Therefore, O(1)

– For real-world rulesets: O(k) which is the optimal we can get

Three HTTP traces: avg(|Si|)<0.04Two WINRPC traces: avg(|Si|)<1.5

3030

Refinement and Extension

• SPM improvement– Allow negative conditions– Handle array cases– Handle associative array cases– Handle mutual exclusive cases

• Extend to Multiple PDU Matching (MPM)– Allow checkpoints.

3131

Outline

• Motivation

• High Speed Matching for Large Rulesets.


• Evaluation

• Research Contribution

High Speed Parsing

• Design a parsing state machine

• Build an automated parsing state machine generator

General V.S. Special Purpose

Keep the whole parsetree in memory

Parsing and matchingon the fly

Parse all the nodes in the tree

Only signature relatedfields (leaf nodes)

V.S.

V.S.

3333

Outline

• Motivation

• High Speed Matching for Large Rulesets.


• Evaluation


34

Evaluation Methodology

• 26GB+ Traces from Tsinghua Univ. (TH), Northwestern (NU) and DARPA

• Run on a P4 3.8Ghz single core PC w/ 4GB memory

• After TCP reassembly and preload the PDUs in memory

• For HTTP we have 794 vulnerability signatures which cover 973 Snort rules.

• For WINRPC we have 45 vulnerability signatures which cover 3,519 Snort rules 34

Fully implemented prototype• 12,000 lines of C++ and

3,000 lines of PythonRelease at:

www.nshield.orgDeployed at a university DC

with up to 106Mbps

3535

Parsing Results

Trace TH DNS

TH WINRPC

NU WINRPC

TH HTTP

NU HTTP

DARPA HTTP

Avg flow len (B) 77 879 596 6.6K 55K 2.1K

Throughput (Gbps)

Binpac

Our parser

0.31

3.43

1.41

16.2

1.11

12.9

2.10

7.46

14.2

44.4

1.69

6.67

Speed up ratio 11.2 11.5 11.6 3.6 3.1 3.9Max. memory per connection (bytes)

15 15 15 14 14 14

3636

Matching Results

Trace TH WINRPC

NU WINRPC

TH HTTP

NU HTTP

DARPA HTTP

Avg flow length (B) 879 596 6.6K 55K 2.1K

Throughput (Gbps)

Sequential

CS Matching

10.68

14.37

9.23

10.61

0.34

2.63

2.37

17.63

0.28

1.85Matching only time

speed up ratio4 1.8 11.3 11.7 8.8

Avg # of Candidates 1.16 1.48 0.033 0.038 0.0023Max. memory per connection (bytes)

27 27 20 20 20

11.08-core

37

Scalability and Accuracy Results

• Create two polymorphic WINRPC exploits which bypass the original Snort rules but detect accurately by our scheme.

• For 10-minute “clean” HTTP trace, Snort reported 42 alerts, NetShield reported 0 alerts. Manually verify the 42 alerts are false positives0 200 400 600 800

01

23

4

# of rules used

Th

rou

gh

pu

t (G

bp

s)

Rule scaling results

Performancedecreasegracefully

Accuracy

3838

Research Contribution

Regular Expression Exists Vul. IDS NetShield

Accuracy Poor Good Good

Speed Good Poor Good

Memory Good ?? Good

Coverage Good ?? Good

Build a better Snort alternative!

• Multiple sig. matching candidate selection algorithm

• Parsing parsing state machine

Make vulnerability signature a practical solutionfor NIDS/NIPS

39

Future work

Social network security

Client Server

Network Security

Web/WebSecurity• WebPropeht[NSDI10

]• WebShield

Data Center Security

40

Q & A

Thanks!

4141

Observations

array

PDU• PDU parse tree

• Leaf nodes are numbers or strings

General V.S. Special Purpose

Keep the whole parsetree in memory

Parsing and matchingon the fly

Parse all the nodes in the tree

Only signature relatedfields (leaf nodes)

V.S.

V.S.

4242

Efficient Parsing with State Machines

• Studied eight protocols: HTTP, FTP, SMTP, eMule, BitTorrent, WINRPC, SNMP and DNS as well as their vulnerability signatures

• Common relationships among leaf nodes

• Pre-construct parsing state machines based on parse trees and vulnerability signatures

Varderive

Sequential Branch Loop Derive(a) (d)(c)(b)

VarVarAutomated parsing state machine generator: UltraPAC

4343

Example for WINRPC• Rectangles are states• Parsing variables: R0 .. R4

• 0.61 instruction/byte for BIND PDU

1 rpc_ver_minor

R4

20*R4

R2++R2£R3

R2 ‹- 0R3 ‹- ncontext

Header BindR0

R0

R1-16

Bind

Bind-ACK

R1

Bind-ACK

1 rpc_vers

1 pfc_flags

1 ptype

2 frag_length

4 packed_drep

6 merge1

1 n_tran_syn

2 ID

16 UUID

1 padding

tran_syn4 UUID_ver

1 ncontext

8 merge2

3 padding

merge3

44

Experiences

• Working in process– In collaboration with MSR, apply the semantic

rich analysis for cloud Web service profiling. To understand why slow and how to improve.

• Interdisciplinary research

• Student mentoring (three undergraduates, six junior graduates)

45

Future Work

• Near term– Web security (browser security, web server security)– Data center security– High speed network intrusion prevention system with

hardware support • Long term research interests

– Combating professional profit-driven attackers will be a continuous arm race

– Online applications (including Web 2.0 applications) become more complex and vulnerable.

– Network speed keeps increasing, which demands highly scalable approaches.

4646

Research Contributions

• Demonstrate vulnerability signatures can be applied to NIDS/NIPS, which can significantly improve the accuracy of current NIDS/NIPS

• Propose the candidate selection algorithm for matching a large number of vulnerability signatures efficiently

• Propose parsing state machine for fast protocol parsing

• Implement the NetShield

47

Comparing With Regex

• Memory for 973 Snort rules: DFA 5.29GB (XFA 863 rules1.08MB), NetShield 2.3MB

• Per flow memory: XFA 36 bytes, NetShield 20 bytes.

• Throughput: XFA 756Mbps, NetShield 1.9+Gbps

(*XFA [SIGCOMM08][Oakland08])

4848

Measure Snort Rules

• Semi-manually classify the rules.1. Group by CVE-ID 2. Manually look at each vulnerability

• Results– 86.7% of rules can be improved by protocol semantic

vulnerability signatures. – Most of remaining rules (9.9%) are web DHTML and

scripts related which are not suitable for signature based approach.

– On average 4.5 Snort rules are reduced to one vulnerability signature.

– For binary protocol the reduction ratio is much higher than that of text based ones. • For netbios.rules the ratio is 67.6.

49

Matcher order

111 iiii BASS

Reduce Si+1 Enlarge Si+1

|| 11 ii BA fixed, put the matcher later, reduce Bi+1

Merging Overhead |Si| (use hash table to calculate in Ai+1, O(1))

50

Matcher order optimization

• Worth buffering only if estmaxB(Mj)<=MaxB

• For Mi in AllMatchers

– Try to clear all the Mj in the buffer which estmaxB(Mj)<=MaxB

– Buffer Mi if (estmaxB(Mi)>MaxB)

– When len(Buf)>Buflen, remove the Mj with minimum estmaxB(Mj)

52

Backup Slides

•

53

Motivation

• Network security has been recognized as the single most important attribute of their networks, according to survey to 395 senior executives conducted by AT&T

• Many new emerging threats make the situation even worse

5454

Candidate merge operation

1 ii AS

Si1 ii AS

Don’t care matcher i+1

requirematcher i+1

In Ai+1

5555

A Vulnerability Signature Example• Data representations

– For all the vulnerability signatures we studied, we only need numbers and strings

– number operators: ==, >, <, >=, <=– String operators: ==, match_re(.,.), len(.).

• Example signature for Blaster wormExample:BIND:rpc_vers==5 && rpc_vers_minor==1 && packed_drep==\x10\x00\x00\x00&& context[0].abstract_syntax.uuid=UUID_RemoteActivationBIND-ACK:rpc_vers==5 && rpc_vers_minor==1CALL:rpc_vers==5 && rpc_vers_minors==1 && packed_drep==\x10\x00\x00\x00&& stub.RemoteActivationBody.actual_length>=40 && matchRE( stub.buffer, /^\x5c\x00\x5c\x00/)

56

System Framework

Content-based signature matching

Streaming packet data

Data path Control pathModules on the critical path

Token Based Signature Generation (TOSG)

Part IIPolymorphic worm signature generation

Modules on the non-critical path

Honeynets/Honeyfarms

Network Situational Awareness

Length Based Signature Generation (LESG)

Part IVNetwork Situational Awareness

To unused IPblocks

Protocol semantic signature matching

Part IIISignature matching engines

Reversiblek-ary sketch monitoring

Sketch based statistical anomaly detection (SSAD)

Local sketch records

Sent out for aggregation

Remote aggregatedsketchrecords Part I

Sketch-basedmonitoring & detection

Scalability




Scalability



Scalability




Scalability


57

Example of Vulnerability Signatures• At least 75%

vulnerabilities are due to buffer overflow

Sample vulnerability signature

• Field length corresponding to vulnerable buffer > certain threshold

• Intrinsic to buffer overflow vulnerability and hard to evade

Vulnerable buffer

Protocol message

Overflow!

58

Old Slides

5959

Conclusions

• A novel network-based vulnerability signature matching engine– Through measurement study on Snort ruleset,

prove the vulnerability signature can improve most of the signatures in NIDS/IPS.

– Proposed parsing state machine for fast parsing

– Propose a candidate selection algorithm for matching a large number of vulnerability signature simultaneously

61

Outline

• Motivation

• Feasibility Study: a measurement approach

• Problem Statement


• High Speed Matching for massive vulnerability Signatures.

• Evaluation

• Conclusions

62

Outline

• Motivation





• Evaluation

• Conclusions

63

Outline

• Motivation




• High Speed Matching for a large number of vulnerability Signatures.

• Evaluation

• Conclusions

64

Outline

• Motivation





• Evaluation

• Conclusions

65

Limitations of Regular Expression Signatures

1010101

10111101

11111100

00010111

Our network

Traffic Filtering

Internet

Signature: 10.*01

XX

Polymorphic attack (worm/botnet) might not have exact regular expression based signature

Polymorphism!

66

What we do?

• Build a NIDS/NIPS with much better accuracy and similar speed comparing with Regular Expression based approaches– Feasibility: Snort ruleset (6,735 signatures) 86.7%

can be improved by vulnerability signatures.– High speed Parsing: 2.7~12 Gbps– High speed Matching:

• Efficient Algorithm for matching massive vulnerability rules• HTTP, 791 vulnerability signatures at ~1Gbps

67

Problem Formulation

• Parsing problem formulation– Given a PDU and the protocol specification as

input, output the set of fields which required by matching.

68

Publications

• Zhichun Li, Lanjia Wang, Yan Chen and Zhi (Judy) Fu, Network-based and Attack-resilient Length Signature Generation for Zero-day Polymorohic Worms, in the Proc. of IEEE ICNP 2007.

• Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons, Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reversible sketches: Enabling monitoring and analysis over high speed data streams, in the IEEE/ACM Transaction on Networking, Volume 15, Issue 5, Oct, 2007

• Zhichun Li, Manan Sanghi, Brian Chavez, Yan Chen and Ming-Yang Kao, Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience, in Proc. of IEEE Symposium on Security and Privacy, 2006

• Zhichun Li, Yan Chen and Aaron Beach, Towards Scalable and Robust Distributed Intrusion Alert Fusion with Good Load Balacing, in Proc. of ACM SIGCOMM LSAD 2006

• Yan Gao, Zhichun Li and Yan Chen, A DoS Resilient Flow-level Intrusion Detection Approach for High-speed Networks, In Proc. Of IEEE ICDCS 2006

• Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons, Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reverse Hashing for High-speed Network Monitoring: Algorithms, Evaluations, and Applications, in the Proc. Of IEEE INFOCOM 2006

69

Current Status

• Part I: Sketch based monitoring & detection– Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons,

Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reversible sketches: Enabling monitoring and analysis over high speed data streams, in the IEEE/ACM Transaction on Networking, Volume 15, Issue 5, Oct, 2007

– Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons, Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reverse Hashing for High-speed Network Monitoring: Algorithms, Evaluations, and Applications, in the Proc. Of IEEE INFOCOM 2006 (252/1400=18%)

– Yan Gao, Zhichun Li and Yan Chen, A DoS Resilient Flow-level Intrusion Detection Approach for High-speed Networks, In Proc. Of IEEE International Conference on Distributed Computing Systems (ICDCS) 2006 (75/536=14%) (Alphabetical order)

• Part II: Polymorphic worm signature generation– TOSG: Zhichun Li, Manan Sanghi, Brian Chavez, Yan Chen and Ming-Yang Kao,

Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience, in Proc. of IEEE Symposium on Security and Privacy, 2006 (23/251=9%)

– LESG: Zhichun Li, Lanjia Wang, Yan Chen and Zhi (Judy) Fu, Network-based and Attack-resilient Length Signature Generation for Zero-day Polymorohic Worms, in the Proc. of IEEE International Conference on Network Protocols (ICNP) 2007 (32/220=14%)

70

Current Status

• Part III: Signature matching engines– Work in progress, will be focus of this talk– Zhichun Li, Gao Xia, Yi Tang, Jian Chen, Ying He, Yan Chen

and Bin Liu, NetShield : Towards High Performance Network-based Semantic Signature Matching, in submission

• Part IV: Network Situational Awareness– Work in process– Zhichun Li, Anup Goyal, Yan Chen and Vern Paxson, Towards

Situational Awareness of Large-Scale Botnet Events using Honeynets, in preparation

– Zhichun Li, Anup Goyal, Yan Chen and Aleksandar Kuzmanovic, P2P Doctor: Measurement and Diagnosis of Misconfigured Peer-to-Peer Traffic, in submission

71

Current Status

• Part I: Sketch based monitoring & detection– Result in [Infocom06,ToN,ICDCS06]

• Part II: Polymorphic worm signature generation– Result in [Oakland06,ICNP07]

• Part III: Signature matching engines– Work in progress, will be focus of this talk

• Part IV: Network Situational Awareness– Work in process

72

Limitations of Exploit Based Signature

1010101

10111101

11111100

00010111

Our network

Traffic Filtering

Internet

Signature: 10.*01

XX

Polymorphic worm might not have exact exploit based signature

Polymorphism!

73

Vulnerability Signature

Work for polymorphic wormsWork for all the worms which target thesame vulnerability

Vulnerability signature traffic filtering

Internet

XX Our network

Vulnerability

XX

towards high performance network defense zhichun li eecs department northwestern university

Documents