Towards High Speed Network Defense

Zhichun LiEECS Deparment

Northwestern University

2

Agenda

• Briefly introduce my thesis work• Dive in high performance vulnerability

signature matching• Future research directions

3

Motivation

Botnets

Worms

Attackers

Professional attackers exploit the enterprise networks for profit $$$

4

Network Level Defense

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

• Only host based detection/prevention is not enough for modern enterprise networks– 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. – Enterprises cannot only reply on their end users for

security protection

5

Challenges

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

• Need to be highly accurate• Adapt fast to the emerging threats• Have good attack coverage.

6

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

Honynethoneyfarms

Packetstreams

Accuracy &adapt fast

Accuracy &adapt fast

Scalability

Accuracy &Scalability & Coverage

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Network-based Intrusion Detection, Prevention, and Forensics System (I)

• Online traffic monitoring and recording [INFOCOM 2006, ToN 2007] (cited by 30+)– 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] [IEEE CG&A, Security Visualization 2006]– Detect TCP SYN flooding, horizontal and vertical scans even w

hen mixed

1

j

H

0 1 K-1…

……

hj(k)

hH(k)

h1(k)

88

• Polymorphic worm signature generation – Token based Signature [IEEE Symposium on Security and

Privacy 2006] (cited by 40+, code requested by Columbia U. UT Austin, Purdue, Georgia Tech, UC Davis, etc)

– Network based Vulnerability Signature [IEEE ICNP 2007] [ NSF Cyber Trust Award]

1010101

10111101

11111100

00010111

Network gatewayInternet

Network-based Intrusion Detection, Prevention, and Forensics System (II)

Our network

99

• NetShield Vulnerability Signature based NIDS/NIPS [under submission] [NSF Cyber Trust Award] (interested by Cisco and Juniper)

Network-based Intrusion Detection, Prevention, and Forensics System (III)

Focus of this talk, details come later

1010

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

soning traffic [under submission]

Network-based Intrusion Detection, Prevention, and Forensics System (IV)

Peers

File Request Flooding

Innocent VictimMisconfigured Traffic

DDoS attack Scenario

11

NetShied: Matching a Large vulnerability Signature Ruleset for High Performance Network Defens

e

12

NetShield Overview NIDS/NIPS (Network Intrusion

Detection/Prevention System) operation

Signature DB

NIDS/NIPS `

`

`

Packets

Securityalerts

• Accuracy• Speed• Attack Coverage

13

State of the art

Pros• Can efficiently match

multiple sigs simultaneously, through DFA

• Can describe the syntactic context

Regular expression (regex) based approachesExample: .*Abc.*\x90+de[^\r\n]{30}

Cons• Limited expressive p

ower• Cannot describe the

semantic context • Inaccurate

14

State of the art

Pros• Directly describe semant

ic context• Very expressive, can ex

press the vulnerability condition exactly

• Accurate

Vulnerability Signature [Wang et al. 04]

Cons• Slow! • Existing approaches all

use sequential matching• Require protocol parsing

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&& stub.RemoteActivationBody.actual_length>=40 && matchRE( stub.buffer, /^\x5c\x00\x5c\x00/)

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Motivation of NetShield

15

Theoretical accuracy limitation of regex

State of the art regex Sig

IDSesNetShield

Existing Vulnerability

Sig IDS

Accuracy HighLowLow

Hig

hS

peed

1616

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!

1717

Research Challenges

• Background– Use protocol semantics to express the vulnerability– Defined on a sequence of PDUs & one predicate for each

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

• Challenges– Matching thousands of vulnerability signatures

simultaneously• Sequential matching match multiple sigs simultaneously

– High speed parsing

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Outline

• Motivation• High Speed Matching for Large Rulesets.• High Speed Parsing• Evaluation• Research Contributions

1919

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/)

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Matching Problem Formulation

• Consider single PDU matching first• 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.

Rule 6: URI.Filename=“fp40reg.dll” && len(Headers[“host”])>300

2121

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.

2222

Matching Algorithms

Candidate Selection Algorithm1.Pre-computation decides the rule order and

matcher order2.Divide-and-conquer comparison w/

matchers and iteratively combine the results efficiently

2323

Step 1: Pre-Computation• Matcher reoder: Put the non-selective matchers

later based on buffering constraint & field arrival order

• Rule reorder:RB1

RB1 RB2

RB1 RB2 RB3 ...RB4

...

Matcher 1 Don’t care of Matcher 1

Extended by Matcher 2

Don’t care of both Matcher 1 & 2

Don’t care of all Matcher 1 to n

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RB1: 1 2 3 RB2: 4 5 6

Step 2: Iterative Matching

RB1: 1 2 3 RB2: 4 5 6 RB3: 7 RB4: 8

RB1: 1 2 3

RB1: 1 2 3 RB2: 4 5 6 RB3: 7

S2 = S1 A2+B2 = {3} {}+{6} = {}+{6} = {6}

S3 = S2 A3+B3 = {6} {}+{} = {6}+{} = {6}

S4 = S3 A4+B4 = {6} {4}+{} = {6}+{} = {6}

RB1: 1 2 3 RB2: 4 5 6 RB3: 7 RB4: 8 RB5: 9S5 = S4 A5+B5 = {6} {6}+{} = {6}+{} = {6}

S1= {3}

PDU={Method=POST, Filename=fp40reg.dll, VARs: name="file"; value~".*\.\./.*", Headers: name="host"; len(value)=450}

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Candidate merge operation

1 ii AS

Si1 ii AS

Don’t care matcher i+1

requirematcher i+1 In Ai+1

2626

Refinement and Extension

• SPM improvement– Allow negative conditions– Handle array case– Handle associate array case– Handle mutual exclusive case– Report the matched rules as early as possible

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

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Outline

• Motivation• High Speed Matching for Large Rulesets.• High Speed Parsing• Evaluation• Research Contribution

2828

Observations

array

PDU• PDU parse tree• Leaf nodes are

integers or strings• Vulnerability signatures

mostly based on leaf nodes

• Observation 1: Only need to parse the fields related to signatures.

• Observation 2: Traditional recursive descent parsers which need one function call per node are too expensive.

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Efficient Parsing with State Machines

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

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

• Common relationship among leaf nodes.

Varderive

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

VarVar

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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_flags1 ptype

2 frag_length4 packed_drep

6 merge1

1 n_tran_syn2 ID

16 UUID1 padding

tran_syn4 UUID_ver

1 ncontext8 merge2

3 padding

merge3

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Outline

• Motivation• High Speed Matching for Large Rulesets.• High Speed Parsing• Evaluation• Research Contributions

32

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 covers

973 Snort rules.• For WINRPC we have 45 vulnerability signatures which covers

3,519 Snort rules 32

Fully implemented prototype• 11,704 lines of C++ and

2,706 lines of Python• Can run on both Linux and

WindowsDeployed at a university DCwith up to 106Mbps

3333

Parsing Results

Trace TH DNS

TH WINRPC

NU WINRPC

TH HTTP

NU HTTP

DARPA HTTP

Throughput (Gbps) Binpac Our parser

0.313.43

1.4116.2

1.1112.9

2.107.46

14.244.4

1.696.67

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

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Matching Results

Trace TH WINRPC

NU WINRPC

TH HTTP

NU HTTP

DARPA HTTP

Throughput (Gbps) Sequential CS Matching

10.6814.37

9.2310.61

0.342.63

2.3717.63

0.281.85

Matching only timespeed up ratio

4 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

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Other Results

• 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]0 200 400 600 800

01

23

4

# of rules used

Thro

ughp

ut (G

bps)

Rule scaling results

PerformancDecreasegracefully

Compare with Regex

3636

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

37

Future work

• 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.

• Future work– Web security (browser security, web server

security)– Data Center security– High Speed Network Intrusion Prevention

System with Hardware Support

38

Long Term Research Challenges

• Combat the professional profit-driven attackers.

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

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

39

Q & A

Thanks!

40

Backup Slides

•

4141

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.

42

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

43

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

Accuracy &adapt fast

Accuracy &Scalability & Coverage

Accuracy &adapt fast

Scalability

Accuracy &Scalability & Coverage

Accuracy &adapt fast

Scalability

Accuracy &Scalability & Coverage

Accuracy &adapt fast

Accuracy &adapt fast

Scalability

Accuracy &Scalability & Coverage

44

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!

45

Old Slides

4646

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

48

Outline

• Motivation• Feasibility Study: a measurement approach• Problem Statement• High Speed Parsing• High Speed Matching for massive

vulnerability Signatures.• Evaluation• Conclusions

49

Outline

• Motivation• Feasibility Study: a measurement approach• Problem Statement• High Speed Parsing• High Speed Matching for massive

vulnerability Signatures.• Evaluation• Conclusions

50

Outline

• Motivation• Feasibility Study: a measurement approach• Problem Statement• High Speed Parsing• High Speed Matching for a large number of

vulnerability Signatures.• Evaluation• Conclusions

51

Outline

• Motivation• Feasibility Study: a measurement approach• Problem Statement• High Speed Parsing• High Speed Matching for massive

vulnerability Signatures.• Evaluation• Conclusions

52

Limitations of Regular Expression Signatures

1010101

10111101

11111100

00010111

Our network

Traffic FilteringInternet

Signature: 10.*01

XX

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

Polymorphism!

53

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

54

Problem Formulation

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

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

55

Publications• Zhichun Li, Lanjia Wang, Yan Chen and Zhi (Judy) Fu, Network-based and At

tack-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

56

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%)

57

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

58

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

59

Limitations of Exploit Based Signature

1010101

10111101

11111100

00010111

Our network

Traffic FilteringInternet

Signature: 10.*01

XX

Polymorphic worm might not have exact exploit based signature

Polymorphism!

60

Vulnerability Signature

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

Vulnerability signature traffic filteringInternet

XX Our network

Vulnerability

XX