Transcript worksummary_netshiel.. - Northwestern University

Towards High Performance Network
Defense
Zhichun Li
EECS Department
Northwestern University
Motivation
Attackers
Botnets
Professional attackers exploit
networks for profit $$$ Worms
2
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
3
Challenges
• Scalable to high speed networks with a
large number of users
• Highly accurate
• Adapt fast to the emerging threats
• Have good attack coverage
4
Network-based Intrusion Detection,
Prevention, and Forensics System
• Framework
Scalability
Accuracy &
Scalability &
Coverage
Packet
streams
(I) Sketch
based monitoring
& detection
Accuracy &
adapt fast
(III) Signature (II) Polymorphic
matching
worm signature
engines
generation
(IV) Network
situational
awareness
Accuracy &
adapt fast
5
High-speed Network Monitoring
and 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
…
h1(k)
K-1
1
…
[IEEE IWQoS 2007]
0 1
…
j
H
hj(k)
hH(k)
6
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]
7
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]
1010101
Internet
Network
gateway
10111101
11111100
Our network
00010111
8
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
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/poisoning traffic [INFOCOM10]
• Analysis of 2TB data across 4 years over 5 /8 IPs
Peers
File Request Flooding
Innocent Victim
Misconfigured Traffic
DDoS attack Scenario
10
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
NetShield: Matching a Large
Vulnerability Signature Ruleset
for High Performance Network
Defense
12
Outline
•
•
•
•
•
Motivation
High Speed Matching for Large Rulesets
High Speed Parsing
Evaluation
Research Contributions
13
NetShield Overview
NIDS/NIPS (Network Intrusion
Detection/Prevention System) operation
Signature
DB
Packets
NIDS/NIPS
`
`
`
Security • Accuracy
alerts
• Speed
• Attack Coverage
14
State Of The Art
Regular expression (regex) based approaches
Used by: Cisco IPS, Juniper IPS, open source Bro
Example: .*Abc.*\x90+de[^\r\n]{30}
Pros
• Can efficiently match multiple sigs simultaneously,
through DFA
• Can describe the syntactic context
15
Cons of Regex
Limited expressive power, cannot describe
semantic context, thus inaccurate
Theoretical prospective
Regex
Protocol Context
Context
Sensitive
grammar
Free
Practical prospective
• HTTP chunk encoding
• DNS label pointers
State Of The Art
Vulnerability Signature [Wang et al. 04]
Blaster Worm (WINRPC) Example:
Vulnerability: design flaws enable the bad
BIND:
inputs lead&&
therpc_vers_minor==1
program to a bad&&
state
rpc_vers==5
packed_drep==\x10\x00\x00\x00
Good
&& context[0].abstract_syntax.uuid=UUID_RemoteActivation state
BIND-ACK:
Bad input
rpc_vers==5
&& rpc_vers_minor==1
CALL:
rpc_vers==5 && rpc_vers_minors==1 && packed_drep==\x10\x00\x00\x00
Bad
Vulnerability
&& opnum==0x00 && stub.RemoteActivationBody.actual_length>=40
state
Signature
&& 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
17
Speed
High
Motivation of NetShield
State of the
art regex Sig
IDSes
NetShield
Theoretical accuracy
limitation of regex
Low
Existing
Vulnerability
Sig IDS
Low
Accuracy
High
18
Motivation
• Desired Features for Signature-based
NIDS/NIPS
– Accuracy (especially for IPS)
– Speed
Cannot capture
vulnerability – Coverage: Large ruleset
condition well!
Regular
Expression
Vulnerability
Accuracy
Relative
Poor
Much Better
Speed
Good
??
Memory
OK
??
Coverage
Good
??
Shield
[sigcomm’04]
Focus of
this work
19
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
20
signature matching
Background
• Vulnerability signature basic
– Use protocol semantics to express vulnerabilities
– Defined on a sequence of PDUs & one predicate for
Blastereach
WormPDU
(WINRPC) Example:
BIND:
– Example: ver==1 && method==“put” && len(buf)>300
rpc_vers==5 && rpc_vers_minor==1 && packed_drep==\x10\x00\x00\x00
&&
context[0].abstract_syntax.uuid=UUID_RemoteActivation
• Data
representations
BIND-ACK:
– For &&
all the
vulnerability signatures we studied, we only
rpc_vers==5
rpc_vers_minor==1
CALL: need numbers and strings
rpc_vers==5
&& rpc_vers_minors==1
&&<,packed_drep==\x10\x00\x00\x00
– number
operators: ==, >,
>=, <=
&& opnum==0x00 && stub.RemoteActivationBody.actual_length>=40
– String operators:
==, match_re(.,.), len(.).
&& matchRE(stub.buffer,
/^\x5c\x00\x5c\x00/)
21
Outline
•
•
•
•
•
Motivation
High Speed Matching for Large Rulesets
High Speed Parsing
Evaluation
Research Contributions
22
Matching Problem Formulation
• Suppose we have n signatures, defined on k
matching dimensions (matchers)
– A matcher is a two-tuple (field, operation) or a fourtuple 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”])>300
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
23
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.
24
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)
25
Matching Algorithms
Candidate Selection Algorithm
1.Pre-computation decides the rule order and
• Integer range checking 
matcher order
balanced binary search tree
• String exact
matching
Trie
2.Decomposition.
Match
each 
matcher
• Regex  DFA (XFA)
separately and iteratively combine the
results efficiently
26
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
Don’t care
Matcher 1
Don’t care
Matcher 1
&2
n
27
Step 2: Iterative Matching
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}
Si  Ai 1
Don’t care
RuleID Method == Filename
== Header == LEN
R1
R2
R3
1
2
DELETE
SiPOST
* matcher i+1 *
Header.php
*
*
3
*
awstats.pl
4
*
fp40reg.dll
5
*
*
Si  Ai 1
require
In Ai+1 len(value)>300
name==“host”;
matcher i+1
name==“User-Agent”; len(value)>544
28
Complexity Analysis
Three HTTP traces:
avg(|Si|)<0.04
• Merging complexity
Two WINRPC
– Need k-1 merging iterations
traces: avg(|Si|)<1.5
– 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
29
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
Outline
•
•
•
•
•
Motivation
High Speed Matching for Large Rulesets.
High Speed Parsing
Evaluation
Research Contribution
31
High Speed Parsing
General V.S. Special Purpose
Keep the whole parse
Parsing and matching
V.S. on the fly
tree in memory
Parse all the nodes
in the tree
Only signature related
V.S. fields (leaf nodes)
• Design a parsing state machine
• Build an automated parsing state machine
generator
Outline
•
•
•
•
•
Motivation
High Speed Matching for Large Rulesets.
High Speed Parsing
Evaluation
Research Contributions
33
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
34
3,519 Snort rules
Parsing Results
Trace
TH
DNS
TH
NU
TH
WINRPC WINRPC 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 14.2 1.69
7.46 44.4 6.67
11.2
Max. memory per 15
11.5
15
11.6
15
3.6
14
Speed up ratio
NU
HTTP
3.1
14
DARPA
HTTP
3.9
14
connection
(bytes)
35
Matching Results
8-core 11.0
Trace
TH
NU
TH
WINRPC WINRPC HTTP
NU
HTTP
DARPA
HTTP
Avg flow length (B)
879
596
6.6K
55K
2.1K
10.68
14.37
4
9.23
10.61
1.8
0.34
2.63
11.3
2.37 0.28
17.63 1.85
11.7 8.8
1.48
27
0.033 0.038 0.0023
20
20
20
Throughput (Gbps)
Sequential
CS Matching
Matching only time
speed up ratio
Avg # of Candidates 1.16
Max. memory per
connection (bytes)
27
36
Scalability and Accuracy Results
Rule scaling results
Throughput (Gbps)
0
1
2
3
4
Performance
decrease
gracefully
0
200
400
600
# of rules used
800
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
37
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
Future work
Client
Server
Network Security
Data Center Security
Web/WebSecurity
• WebPropeht[NSDI10]
• WebShield
Social network security
39
Q&A
Thanks!
40
Observations
• PDU  parse tree
• Leaf nodes are
numbers or strings
PDU
array
General V.S. Special Purpose
Keep the whole parse
Parsing and matching
V.S. on the fly
tree in memory
Parse all the nodes
in the tree
Only signature related
V.S. fields (leaf nodes)
41
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
Automated parsing state
machine
Var
Var
generator: UltraPAC
derive
Var
Sequential
Branch
Loop
Derive
(a)
(b)
(c)
(d)
• Pre-construct parsing state machines based
on parse trees and vulnerability signatures 42
Example for WINRPC
• Rectangles are states
• Parsing variables: R0 .. R4
• 0.61 instruction/byte for BIND PDU
R1-16
8 merge2
1 ncontext
3 padding
Bind-ACK
1
rpc_vers
1 rpc_ver_minor
R0 1
ptype
Header 1
pfc_flags
R0
4 packed_drep
Bind
R1 2 frag_length
6
merge1
merge3
R4
20*R4
2
ID
1 n_tran_syn
1 padding
16 UUID
4 UUID_ver
tran_syn
Bind-ACK
R2 ‹- 0
R3 ‹- ncontext
Bind
R2++
R2£R3
43
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)
44
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.
45
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
46
• Implement the NetShield
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])
47
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.
48
Matcher order
Si 1  Si  Ai 1  Bi 1
Reduce Si+1 Enlarge Si+1
Merging Overhead |Si| (use hash table to calculate
in Ai+1, O(1))
| Ai 1  Bi 1 | fixed, put the matcher later, reduce Bi+1
49
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)
50
51
•
Backup Slides
52
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
53
Candidate merge operation
Si  Ai 1
Don’t care
matcher i+1
Si
Si  Ai 1
require
matcher i+1
In Ai+1
54
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 worm
Example:
BIND:
rpc_vers==5 && rpc_vers_minor==1 && packed_drep==\x10\x00\x00\x00
&& context[0].abstract_syntax.uuid=UUID_RemoteActivation
BIND-ACK:
rpc_vers==5 && rpc_vers_minor==1
CALL:
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/)
55
System Framework
Accuracy &
Scalability &
Coverage
Sent out for
aggregation
Reversible
k-ary sketch
monitoring
Local
sketch
records
Remote
aggregated
sketch
records
Sketch based
statistical anomaly
detection (SSAD)
Part III
Streaming
packet
data
Signature
matching
Content-based
engines
signature matching
Token Based Signature
Generation (TOSG)
Protocol semantic
signature matching
To unused IP
blocks
Data path
Length Based Signature
Generation (LESG)
Network
Situational
Awareness
Honeynets/
Honeyfarms
Control path
Modules on
the critical
path
Modules on
the non-critical
path
Scalability
Part I
Sketchbased
monitoring
& detection
Accuracy &
adapt fast
Part II
Polymorphic
worm
signature
generation
Part IV
Network
Situational
Awareness
Accuracy &
adapt56fast
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
Overflow!
Protocol message
Vulnerable
buffer
57
Old Slides
58
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
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 a large number
of vulnerability Signatures.
• Evaluation
• Conclusions
63
Outline
• Motivation
• Feasibility Study: a measurement
approach
• Problem Statement
• High Speed Parsing
• High Speed Matching for massive
vulnerability Signatures.
• Evaluation
• Conclusions
64
Limitations of Regular Expression
Signatures
Signature: 10.*01
1010101
10111101
Internet
Traffic
Filtering
X
X
11111100
Our network
00010111
Polymorphism!
Polymorphic attack (worm/botnet)
might not have exact regular
expression based signature
65
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
66
Problem Formulation
• Parsing problem formulation
– Given a PDU and the protocol specification as
input, output the set of fields which required
by matching.
67
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 68
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%)
69
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 Networkbased 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
70
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
71
Limitations of Exploit Based Signature
Signature: 10.*01
1010101
10111101
Internet
Traffic
Filtering
X
X
11111100
Our network
00010111
Polymorphism!
Polymorphic worm might not have
exact exploit based signature
72
Vulnerability Signature
Internet
Vulnerability
signature traffic
filtering
X
X
Our network
X
X
Vulnerability
Work for polymorphic worms
Work for all the worms which target the
same vulnerability
73