Xu, Guangquan, Feng, Meiqi, Jiao, Litao, Liu, Jian, Dai, Hong-Ning, Wang, Ding, Panaousis, Emmanouil ORCID: https://orcid.org/0000-0001-7306-4062 and Zheng, Xi (2022) MFF-AMD: multivariate feature fusion for Android malware detection. In: CollaborateCom 2021: Networking, Applications and Worksharing 17th EAI. Virtual Event, October 16-18, 2021, Proceedings, Part I. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 406), 406 (ch. 10). Springer Link, Cham, pp. 368-385. ISBN 9783030926342 ; 9783030926359

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Abstract

Researchers have turned their focus on leveraging either dynamic or static features extracted from applications to train AI algorithms to identify malware precisely. However, the adversarial techniques have been continuously evolving and meanwhile, the code structure and application function have been designed in complex format. This makes Android malware detection more challenging than before. Most of the existing detection methods may not work well on recent malware samples. In this paper, we aim at enhancing the detection accuracy of Android malware through machine learning techniques via the design and development of our system called MFF-AMD. In our system, we first extract various features through static and dynamic analysis and obtain a multiscale comprehensive feature set. Then, to achieve high classification performance, we introduce the Relief algorithm to fuse the features, and design four weight distribution algorithms to fuse base classifiers. Finally, we set the threshold to guide MFF-AMD to perform static or hybrid analysis on the malware samples. Our experiments performed on more than 25,000 applications from the recent five-year dataset demonstrate that MFF-AMD can effectively detect malware with high accuracy.

Item Type:	Conference Proceedings
Title of Proceedings:	CollaborateCom 2021: Networking, Applications and Worksharing 17th EAI. Virtual Event, October 16-18, 2021, Proceedings, Part I.
Uncontrolled Keywords:	malware detection, hybrid analysis, weight distribution, multivariate feature fusion
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Internet of Things and Security Research Centre (ISEC) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	22 Sep 2023 16:37
URI:	http://gala.gre.ac.uk/id/eprint/34783

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