roberto paleari,universit`a degli studi di milano lorenzo martignoni,universit`a degli studi di...

Roberto Paleari, Universit`a degli Studi di Milano

Lorenzo Martignoni, Universit`a degli Studi di Udine

Emanuele Passerini, Universit`a degli Studi di Milano

Drew Davidson, University of Wisconsin

Matt Fredrikson, University of Wisconsin

Jon Giffin, Georgia Institute of Technology

Somesh Jha University of Wisconsin

Automatic Generation of Remediation Procedures for

Malware Infections

2010 USENIX Security Symposium

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Outline

IntroductionRelated WorkSystem OverviewSystem DetailsEvaluationDiscussionConclusion

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Introduction

After infection, Format disk and re-install OS

Data backups Commercial anti-malware software

*TRIES TO* Revert the effects performed by malware Unstable, or even failed

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Introduction

In this work… Given binary malware Automatically generate remediation procedures Do not require the information relating to the

infection 98% of the harmful effects reverted

http://pages.cs.wisc.edu/~mfredrik/remediate/

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Related Work

Behavior-based malware analysis Dynamic analysis:

A layered architecture for detecting malicious behaviors, RAID 2008

Panorama: Capturing system-wide information flow for malware detection and analysis, ACM CCS 2007

Behavior-based detection Effective and efficient malware detection at the end host,

USENIX Security Symposium 2009 Clustering

Scalable, behavior-based malware clustering, NDSS 2009

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Related Work

Execution of Untrusted Applications Back to the future: A framework for automatic

malware removal and system repair, ACSAC 2006 One-way isolation: An effective approach for realizing

safe execution environments, NDSS 2005

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System Overview

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System Overview

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System Overview

High-Level Behavior Extraction Analyze the semantics of a program to produce a

sequence of meaningful behaviors

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System Overview

Behavior Generalization Attempt to over-approximate existing paths, thus

encompassing future paths Cluster all instances of the same high-level behavior

together Analyze each cluster to generalize the arguments

c:\windows\po[[:alpha:]]{3}.exe

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System Overview

Remediation Procedure Generation Attempt to match each resource (file, process, or

registry key) on the system against the constraints associated with each generalized high-level behavior

c:\windows\po[[:alpha:]]{3}.exe

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System Details

High-Level Behavior Extraction Use QEMU to monitor a malware for its system call

trace

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System Details

Behavior Clustering

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System Details

Comparison isomorphic( )

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System Details

BehaviorGeneralization

Probabilistic finite-state automaton (PFSA) Simulated beam annealing algorithm

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System Details

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System Details

Generating Concrete Remediation Procedures Newly-created resources

DropAndAutostart(file,data,key,value,regdata )

DropAndAutostart(“c : \windows\po[[: alpha :]]{3}.exe”, data, “...Windows\CurrentVersion\Run”,“(vq|qv)”,“po[[:alpha:]]{3}.exe” )

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System Details

Generating Concrete Remediation Procedures Infected Resources

Deleted Resources Not implemented

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Evaluation

Over 200 malicious programsExecute a sample 3 times in 5 different

environments to collect trace dataInfect 25 test environments which are all

distinct from those used to collect tracesExecute the generated remediation

procedureCompare the remediated state to the original

state

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Evaluation

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Evaluation

False positives One sample: very general regular expression

*.exe Future work

Context-free grammars

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Discussion

Limitation Finding all high-level malicious behaviors can not be

guaranteed. Specific environment is required Not enough generalizing traces

Evasion techniques

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Conclusion

Automatically generating malware remediation procedures

Dynamic analysisBehavior generalizationEffectively remediate many possible

executionsGood performanceLow false rate