Towards High Performance Network Defense

1
Towards High Performance Network Defense

• Zhichun Li
• EECS Department
• Northwestern University

2
Motivation
Attackers
Botnets
Professional attackers exploit networks for
profit
Worms
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
  915 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

Scalability
(I) Sketch based monitoring detection
Accuracy Scalability Coverage
Accuracy adapt fast
(III) Signature matching engines
(II) Polymorphic worm signature generation
Packet streams
(IV) Network situational awareness
Accuracy adapt fast
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 CGA, Security Visualization 2006
• Online stealthy botnet scan detection
• IEEE IWQoS 2007

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

8
Polymorphic Worm Signature Generation

• 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

Network gateway
Internet
Our network
8
9
Online Protocol Parsing and Signature Matching

• 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

9
10
Network Situational Awareness

• Large-scale botnet and P2P misconfiguration event
  situational-aware forensics
• Botnet attack target/strategy inference
  ASIACCS09
• Root cause analysis of the P2P misconfiguration/po
  isoning traffic INFOCOM10
• Analysis of 2TB data across 4 years over 5 /8 IPs

10
11
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

12
NetShield Matching a Large Vulnerability
Signature Ruleset for High Performance Network
Defense
13
Outline

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

13
13
14
NetShield Overview

• NIDS/NIPS (Network Intrusion
  Detection/Prevention System) operation

NIDS/NIPS
Packets

• Accuracy
• Speed
• Attack Coverage

Security alerts
15
State Of The Art
Regular expression (regex) based approaches
Used by Cisco IPS, Juniper IPS, open source Bro
Example .Abc.\x90de\r\n30

• Pros
• Can efficiently match multiple sigs
  simultaneously, through DFA
• Can describe the syntactic context

16
Cons of Regex
Limited expressive power, cannot describe
semantic context, thus inaccurate
Theoretical prospective
Protocol grammar
Practical prospective

• HTTP chunk encoding
• DNS label pointers

17
State Of The Art
Vulnerability Signature Wang et al. 04
Blaster Worm (WINRPC) Example BIND rpc_vers5
rpc_vers_minor1 packed_drep\x10\x00\x00\
x00 context0.abstract_syntax.uuidUUID_Remote
Activation BIND-ACK rpc_vers5
rpc_vers_minor1 CALL rpc_vers5
rpc_vers_minors1 packed_drep\x10\x00\x00\x0
0 opnum0x00 stub.RemoteActivationBody.actu
al_lengthgt40 matchRE(stub.buffer,
/\x5c\x00\x5c\x00/)

• Pros
• Directly describe semantic context
• Very expressive, can express the vulnerability
  condition exactly
• Accurate

• Cons
• Slow!
• Existing approaches all use sequential matching
• Require protocol parsing

18
Motivation of NetShield
18
19
Motivation

• Desired Features for Signature-based NIDS/NIPS
• Accuracy (especially for IPS)
• Speed
• Coverage Large ruleset

Cannot capture vulnerability condition well!
Shield sigcomm04
Regular Expression Vulnerability
Accuracy Relative Poor Much Better
Speed Good ??
Memory OK ??
Coverage Good ??
19
20
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

20
21
Background

• Vulnerability signature basic
• Use protocol semantics to express vulnerabilities
• Defined on a sequence of PDUs one predicate for
  each PDU
• Example ver1 methodput len(buf)gt300
• Data representations
• For all the vulnerability signatures we studied,
  we only need numbers and strings
• number operators , gt, lt, gt, lt
• String operators , match_re(.,.), len(.).

Blaster Worm (WINRPC) Example BIND rpc_vers5
rpc_vers_minor1 packed_drep\x10\x00\x00\
x00 context0.abstract_syntax.uuidUUID_Remote
Activation BIND-ACK rpc_vers5
rpc_vers_minor1 CALL rpc_vers5
rpc_vers_minors1 packed_drep\x10\x00\x00\x0
0 opnum0x00 stub.RemoteActivationBody.actu
al_lengthgt40 matchRE(stub.buffer,
/\x5c\x00\x5c\x00/)
21
22
Outline

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

22
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.Filenamefp40reg.dll
len(Headershost)gt300
RuleID Method Filename Header LEN
1 DELETE
2 POST Header.php
3 awstats.pl
4 fp40reg.dll namehost len(value)gt300
5 nameUser-Agent len(value)gt544
24
Matching Problem Formulation

• Challenges for Single PDU matching problem (SPM)
• Large number of signatures n
• Large number of matchers k
• Large number of dont cares
• Cannot reorder matchers arbitrarily -- buffering
  constraint
• Field dependency
• Arrays, associative arrays
• Mutually exclusive fields.

24
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)

26
Matching Algorithms

• Candidate Selection Algorithm
• Pre-computation decides the rule order and
  matcher order
• 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)

26
27
Step 1 Pre-Computation

• Optimize the matcher order based on buffering
  constraint field arrival order
• Rule reorder

1
Require Matcher 1
Require Matcher 1
Require Matcher 2
Dont care Matcher 1
Dont care Matcher 1 2
n
28
Step 2 Iterative Matching
PDUMethodPOST, Filenamefp40reg.dll, Header
namehost, len(value)450
S12 Candidates after match Column 1 (method)
S2
B2
2
444
RuleID Method Filename Header LEN
1 DELETE
2 POST Header.php
3 awstats.pl
4 fp40reg.dll namehost len(value)gt300
5 nameUser-Agent len(value)gt544
R1
R2
R3
28
29
Complexity Analysis
Three HTTP traces avg(Si)lt0.04 Two WINRPC
traces avg(Si)lt1.5

• 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

30
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.

30
31
Outline

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

31
32
High Speed Parsing
General V.S. Special Purpose
Keep the whole parse tree in memory
Parsing and matching on the fly
V.S.
Parse all the nodes in the tree
Only signature related fields (leaf nodes)
V.S.

• Design a parsing state machine
• Build an automated parsing state machine generator

33
Outline

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

33
34
Evaluation Methodology

• Fully implemented prototype
• 12,000 lines of C and 3,000 lines of Python
• Release at
• www.nshield.org
• Deployed at a university DC
• with up to 106Mbps

• 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
35
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 3.9
Max. memory per connection (bytes) 15 15 15 14 14 14
35
36
Matching Results
11.0
8-core
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 1.85
Matching only time speed up ratio 4 1.8 11.3 11.7 8.8
Avg of Candidates 1.16 1.48 0.033 0.038 0.0023
Max. memory per connection (bytes) 27 27 20 20 20
36
37
Scalability and Accuracy Results
Rule scaling results
Accuracy

• 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 positives

Performance decrease gracefully
38
Research Contribution
Make vulnerability signature a practical
solution for NIDS/NIPS
Regular Expression Exists Vul. IDS NetShield
Accuracy Poor Good Good
Speed Good Poor Good
Memory Good ?? Good
Coverage Good ?? Good

• Multiple sig. matching ? candidate selection
  algorithm
• Parsing ? parsing state machine

Build a better Snort alternative!
38
39
Future work
Client
Server
Network Security
Data Center Security

• Web/WebSecurity
• WebPropehtNSDI10
• WebShield

• Social network security

40

• Q A
• Thanks!

41
Observations

• PDU ? parse tree
• Leaf nodes are numbers or strings

PDU
array
General V.S. Special Purpose
Keep the whole parse tree in memory
Parsing and matching on the fly
V.S.
Parse all the nodes in the tree
Only signature related fields (leaf nodes)
V.S.
41
42
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

Automated parsing state machine generator
UltraPAC
42
43
Example for WINRPC

• Rectangles are states
• Parsing variables R0 .. R4
• 0.61 instruction/byte for BIND PDU

43
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.

46
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

46
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 1.9Gbps
• (XFA SIGCOMM08Oakland08)

48
Measure Snort Rules

• Semi-manually classify the rules.
• Group by CVE-ID
• 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.

48
49
Matcher order
Reduce Si1
Enlarge Si1
Merging Overhead Si (use hash table to
calculate in Ai1, O(1))
fixed, put the matcher later, reduce Bi1
50
Matcher order optimization

• Worth buffering only if estmaxB(Mj)ltMaxB
• For Mi in AllMatchers
• Try to clear all the Mj in the buffer which
  estmaxB(Mj)ltMaxB
• Buffer Mi if (estmaxB(Mi)gtMaxB)
• When len(Buf)gtBuflen, remove the Mj with minimum
  estmaxB(Mj)

51
(No Transcript)
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 ATT
• Many new emerging threats make the situation even
  worse

54
Candidate merge operation
54
55
A Vulnerability Signature Example

• Data representations
• For all the vulnerability signatures we studied,
  we only need numbers and strings
• number operators , gt, lt, gt, lt
• String operators , match_re(.,.), len(.).
• Example signature for Blaster worm

Example BIND rpc_vers5 rpc_vers_minor1
packed_drep\x10\x00\x00\x00
context0.abstract_syntax.uuidUUID_RemoteActivat
ion BIND-ACK rpc_vers5 rpc_vers_minor1 CAL
L rpc_vers5 rpc_vers_minors1
packed_drep\x10\x00\x00\x00
stub.RemoteActivationBody.actual_lengthgt40
matchRE( stub.buffer, /\x5c\x00\x5c\x00/)
55
56
System Framework
Scalability
Scalability
Scalability
Scalability
Accuracy Scalability Coverage
Accuracy Scalability Coverage
Accuracy Scalability Coverage
Accuracy Scalability Coverage
Accuracy adapt fast
Accuracy adapt fast
Accuracy adapt fast
Accuracy adapt fast
Accuracy adapt fast
57
Example of Vulnerability Signatures

• At least 75 vulnerabilities are due to buffer
  overflow
• Sample vulnerability signature
• Field length corresponding to vulnerable buffer gt
  certain threshold
• Intrinsic to buffer overflow vulnerability and
  hard to evade

Overflow!
Protocol message
Vulnerable buffer
58
Old Slides
59
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

59
60
Outline

• Motivation
• Feasibility Study a measurement approach
• Problem Statement
• High Speed Parsing
• High Speed Matching for massive vulnerability
  Signatures.
• Evaluation
• Conclusions

61
Outline

• Motivation
• Feasibility Study a measurement approach
• Problem Statement
• High Speed Parsing
• High Speed Matching for massive vulnerability
  Signatures.
• Evaluation
• Conclusions

62
Outline

• Motivation
• Feasibility Study a measurement approach
• Problem Statement
• High Speed Parsing
• High Speed Matching for massive vulnerability
  Signatures.
• Evaluation
• Conclusions

63
Outline

• Motivation
• Feasibility Study a measurement approach
• Problem Statement
• High Speed Parsing
• High Speed Matching for a large number of
  vulnerability Signatures.
• Evaluation
• Conclusions

64
Outline

• Motivation
• Feasibility Study a measurement approach
• Problem Statement
• High Speed Parsing
• High Speed Matching for massive vulnerability
  Signatures.
• Evaluation
• Conclusions

65
Limitations of Regular Expression Signatures
Signature 10.01
Traffic Filtering
Internet
Our network
X
X
Polymorphism!
Polymorphic attack (worm/botnet) might not have
exact regular expression based signature
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 2.712 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 (252/140018)
• 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 (75/53614) (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
  (23/2519)
• 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 (32/22014)

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
Signature 10.01
Traffic Filtering
Internet
Our network
X
X
Polymorphism!
Polymorphic worm might not have exact exploit
based signature
73
Vulnerability Signature
Vulnerability signature traffic filtering
Internet
X
X
Our network
X
X
Vulnerability

• Work for polymorphic worms
• Work for all the worms which target the
• same vulnerability