Android Malware Detection: an Eigenspace Analysis Approach

Abstract

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

7 pages, 4 figures, conference

References in corpus (3)

• A New Android Malware Detection Approach Using Bayesian Classification
• Analysis of Bayesian Classification based Approaches for Android Malware Detection
• Android Malware Detection Using Parallel Machine Learning Classifiers

Cited by in corpus (4)

• N-opcode Analysis for Android Malware Classification and Categorization
• EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning
• DynaLog: An automated dynamic analysis framework for characterizing Android applications
• Improving Dynamic Analysis of Android Apps Using Hybrid Test Input Generation