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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01.07.2015
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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. |
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| AbstractList | 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. |
| Author | Muttik, Igor Yerima, Suleiman Y. Sezer, Sakir |
| Author_xml | – sequence: 1 givenname: Suleiman Y. surname: Yerima fullname: Yerima, Suleiman Y. email: s.yerima@qub.ac.uk organization: Centre for Secure Inf. Technol. (CSIT), Queen's Univ. Belfast, Belfast, UK – sequence: 2 givenname: Sakir surname: Sezer fullname: Sezer, Sakir email: s.sezer@qub.ac.uk organization: Centre for Secure Inf. Technol. (CSIT), Queen's Univ. Belfast, Belfast, UK – sequence: 3 givenname: Igor surname: Muttik fullname: Muttik, Igor email: mig@mcafee.com organization: McAfee Labs. (Part of Intel Security), Aylesbury, UK |
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| SubjectTerms | Accuracy Android Androids eigenspace eigenvectors Feature extraction Humanoid robots Machine learning algorithms Malware malware detection mobile security statistical machine learning Training |
| Title | Android malware detection: An eigenspace analysis approach |
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