CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning

Nur Khairani, Kamarudin and Ahmad Firdaus, Zainal Abidin and Azlee, Zabidi and Mohd Faizal, Ab Razak (2023) CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 396-401. (192961). ISBN 979-835031093-1

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Abstract

Today many smart devices are running on Android systems. With the increasing popularity of Android, mobile malware continuously evolves as well, and further attacks Android operating systems. To address these shortcoming issues many security experts use different approaches to detect malware based on various static features. However, by considering only the statistical features, the potential semantic information such as the behavioral feature of the code is overlooked. To leverage the existing static analysis techniques, this study proposes CAGDeep, to reflect deep semantic information of malware samples. The novelty of our study lies in the Force Atlas 2 call graph development to capture malware behavior patterns. Afterwards, this study adopts Convolutional Neural Network (CNN) for malware detection and classification algorithm. We compare CAGDeep with a state-of-the-art Android malware detection approach. Our evaluation results demonstrate that CAGDeep can achieve 80% accuracy for detecting malware.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Call graph; Feature selection; Machine learning; Mobile malware
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	16 Apr 2024 04:16
Last Modified:	16 Apr 2024 04:16
URI:	http://umpir.ump.edu.my/id/eprint/40367
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