Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2014

Abstract

The drastic increase of Android malware has led to a strong interest in developing methods to automate the malware analysis process. Existing automated Android malware detection and classification methods fall into two general categories: 1) signature-based and 2) machine learning-based. Signature-based approaches can be easily evaded by bytecode-level transformation attacks. Prior learning-based works extract features from application syntax, rather than program semantics, and are also subject to evasion. In this paper, we propose a novel semantic-based approach that classifies Android malware via dependency graphs. To battle transformation attacks, we extract a weighted contextual API dependency graph as program semantics to construct feature sets. To fight against malware variants and zero-day malware, we introduce graph similarity metrics to uncover homogeneous application behaviors while tolerating minor implementation differences. We implement a prototype system, DroidSIFT, in 23 thousand lines of Java code. We evaluate our system using 2200 malware samples and 13500 benign samples. Experiments show that our signature detection can correctly label 93% of malware instances; our anomaly detector is capable of detecting zero-day malware with a low false negative rate (2%) and an acceptable false positive rate (5.15%) for a vetting purpose.

Keywords

Android, Anomaly detection, Graph similarity, Malware classification, Semantics-aware, Signature detection

Discipline

Information Security

Research Areas

Cybersecurity; Information Systems and Management

Publication

Proceedings of the 21st ACM Conference on Computer and Communications Security, Scottsdale, USA, 2014 November 3-7

First Page

1105

Last Page

1116

Identifier

10.1145/2660267.2660359

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1145/2660267.2660359

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