Android malware detection: An eigenspace analysis approach

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 ba...

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Vydané v:2015 Science and Information Conference (SAI) s. 1236 - 1242
Hlavní autori: Yerima, Suleiman Y., Sezer, Sakir, Muttik, Igor
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2015
Predmet:
Accuracy
Android
Androids
eigenspace
eigenvectors
Feature extraction
Humanoid robots
Machine learning algorithms
Malware
malware detection
mobile security
statistical machine learning
Training
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Shrnutí: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.
DOI:10.1109/SAI.2015.7237302