network based and attack resilient length signature generation for zero day polymorphic worms
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Network-based and Attack-resilient Length Signature Generation for Zero-day Polymorphic Worms. Zhichun Li 1 , Lanjia Wang 2 , Yan Chen 1 and Judy Fu 3. 1 Lab for Internet and Security Technology (LIST), Northwestern Univ. 2 Tsinghua University, China 3 Motorola Labs, USA.

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network based and attack resilient length signature generation for zero day polymorphic worms

Network-based and Attack-resilient Length Signature Generation for Zero-day Polymorphic Worms

Zhichun Li1, Lanjia Wang2, Yan Chen1and Judy Fu3

1 Lab for Internet and Security Technology (LIST), Northwestern Univ.

2 Tsinghua University, China

3 Motorola Labs, USA

limitations of exploit based signature
1010101

10111101

11111100

00010111

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

vulnerability signature
Vulnerability Signature

Work for polymorphic worms

Work for all the worms which target the

same vulnerability

Vulnerability signature trafficfiltering

Internet

X

X

Our network

X

X

Unknown

Vulnerability

Better!

benefits of network based detection
Benefits of Network Based Detection
  • At the early stage of the worm, only limited worm samples.
  • Host based sensors can only cover limited IP space, which might have scalability issues.

Internet

Gateway routers

Our network

Host based

detection

Early Detection!

design space and related work
Design Space and Related Work

Network Based

Host Based

  • Most host approaches depend on lots of host information, such as source/binary code of the vulnerable program, vulnerability condition, execution traces, etc.

Exploit Based

Vulnerability Based

outline
Outline

Motivation and Related Work

Design of LESG

Problem Statement

Three Stage Algorithm

Attack Resilience Analysis

Evaluation

Conclusions

basic ideas
Basic Ideas
  • At least 75% vulnerabilities are due to buffer overflow
  • Intrinsic to buffer overflow vulnerability and hard to evade
  • However, there could be thousands of fields to select the optimal field set is hard

Overflow!

Protocol message

Vulnerable buffer

framework
Framework

ICDCS06, INFOCOM06, TON

outline1
Outline

Motivation and Related Work

Design of LESG

Problem Statement

Three Stage Algorithm

Attack Resilience Analysis

Evaluation

Conclusions

length based signature definition
Length-based Signature Definition

100

Length Signature (Name,100)

Name

Type

Class

TTL

RDlength

RDATA

Length Signature

RDATA

Vulnerable

Signature Set

{(Name,100), (Class,50), (RDATA,300)}

“OR” relationship

Ground truth signature

(RDATA,315)

Buffer length!

problem formulation
Problem Formulation

Worms which are not covered in the suspicious pool are at most 

Suspicious pool

LESG

Signature

Normal pool

Minimize the false positives in the normal pool

With noise

NP-Hard!

outline2
Outline

Motivation and Related Work

Design of LESG

Problem Statement

Three Stage Algorithm

Attack Resilience Analysis

Evaluation

Conclusions

stages i and ii
Stages I and II

Trade off between specificity and sensitivityScore function Score(COV,FP)

COV≥1%FP≤0.1%

Stage I: Field Filtering

Stage II: Length Optimization

stage iii
Stage III
  • Find the optimal set of fields as the signature with high coverage and low false positive
  • Separate the fields to two sets, FP=0 and FP>0
    • Opportunistic step (FP=0)
    • Attack Resilience step (FP>0)
  • The similar greedy algorithm for each step
stage iii cont
Stage III (cont.)

Name

Type

Class

TTL

Comments

RDATA

Stage ICOV0≥1%FP0≤0.1%

Residual coverage≥5%

50%

0.05%

(RDATA,300) [50%,0.05%]

(Name,100) [40%,0.03%]

(Class,50) [35%,0.09%]

(Comments,2000) [10%,0.1%]

suspicious

normal

stage iii cont1
Stage III (cont.)

Name

Type

Class

TTL

Comments

RDATA

Stage ICOV0≥1%FP0≤0.1%

Residual coverage≥5%

50%

0.05%

{(RDATA,300)}

(Class,50) [25%,0.02%]

(Name,100) [3%,0.08%]

(Comments,2000) [1%,0.05%]

suspicious

normal

stage iii cont2
Stage III (cont.)

Name

Type

Class

TTL

Comments

RDATA

Stage ICOV0≥1%FP0≤0.1%

Residual coverageγ≥5%

(50+25)%

(0.05+0.02)%

{(RDATA,300),(Class,50)}

(Class,50) [25%,0.02%]

(Name,100) [3%,0.08%]

(Comments,2000) [1%,0.05%]

suspicious

normal

attack resilience bounds
Attack Resilience Bounds
  • Depend on whether deliberated noise injection (DNI) exists, we get different bounds
  • With 50% noise in the suspicious pool, we can get the worse case bound FN<2% and FP<1%
  • In practice, the DNI attack can only achieve FP<0.2%
  • Resilient to most proposed attacks (proposed in other papers)
outline3
Outline

Motivation and Related Work

Design of LESG

Problem Statement

Three Stage Algorithm

Attack Resilience Analysis

Evaluation

Conclusions

methodology
Methodology
  • Protocol parsing with Bro and BINPAC (IMC2006)
  • Worm workload
    • Eight polymorphic worms created based on real world vulnerabilities including CodeRed II and Lion worms.
    • DNS, SNMP, FTP, SMTP
  • Normal traffic data
    • 27GB from a university gateway and 123GB email log
results
Results
  • Single/Multiple worms with noise
    • Noise ratio: 0~80%
    • False negative: 0~1% (mostly 0)
    • False positive: 0~0.01% (mostly 0)
  • Pool size requirement
    • 10 or 20 flows are enough even with 20% noises
  • Speed results
    • With 500 samples in suspicious pool and 320K samples in normal pool, For DNS, parsing 58 secs, LESG 18 secs
conclusions
Conclusions

A novel network-based automated worm signature generation approach

  • Work for zero day polymorphic worms with unknown vulnerabilities
  • First work which is both Vulnerability based and Network based using length signature for buffer overflow vulnerabilities
  • Provable attack resilience
  • Fast and accurate through experiments
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