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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.
Original language | English |
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Title of host publication | Proceedings of the 2015 Science and Information Conference |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1236-1242 |
Number of pages | 7 |
DOIs | |
Publication status | Published - 03 Sep 2015 |
Event | 2015 SAI Conference - London, United Kingdom Duration: 28 Jul 2015 → 30 Sep 2015 |
Conference
Conference | 2015 SAI Conference |
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Country | United Kingdom |
City | London |
Period | 28/07/2015 → 30/09/2015 |
Keywords
- malware detection
- machine learning
- data mining
- eigenvectors
- eigenvalue analysis
- mobile security
- Android
- eigenspace
- static analysis
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Projects
- 1 Active
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R1518ECI: CSIT 2 - EPSRC / TSB
O'Neill, M., Bustard, J., Cao, X., Farshim, P., Kurugollu, F., Liu, W., McCanny, J. V., McLaughlin, K., Miller, P., O'Kane, P., O'Sullivan, E. & Sezer, S.
23/03/2015 → …
Project: Research