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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

Adebayo, Olawale Surajudeen and Abdul Aziz, Normaziah (2014) Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization. In: 2014 World Congress on Information and Communication Technologies (WICT 2014), 8-10 December 2014, Melaka, Malaysia.

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Abstract

Several machine learning techniques based on supervised learning have been adopted in the classification of malware. However, only supervised learning techniques have proofed insufficient for malware classification task. This paper presents a classification of android malware using candidate detectors generated from an unsupervised association rule of Apriori algorithm improved with particle swarm optimization to train three different supervised classifiers. In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. Using a number of candidate detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed combined technique has remarkable benefits over the detection using only supervised or unsupervised learners.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 5505/48604
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: Assoc. Pro Normaziah Abdul Aziz
Date Deposited: 16 Feb 2016 14:20
Last Modified: 06 Jun 2016 02:55
URI: http://irep.iium.edu.my/id/eprint/48604

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