Enhancing malware detection in Android application by incorporating broadcast receivers. (24th November 2021)

• Record Type: Journal Article
• Authors: Bisgin, Halil; Mohsen, Fadi; Nwobodo, Vincent; Havens, Rachael
• Is Part Of: International journal of information privacy, security and integrity. Volume 5:Number 1(2021)
• Page Start: 36
• Page End: 68
• Publication Date: 2021-11-24
• Subjects: Android -- broadcast receiver -- privacy -- permissions -- support vector machines -- SVMs -- association rule mining -- feature selection; Computer security -- Periodicals; Computer networks -- Access control -- Periodicals; Information technology -- Security measures -- Periodicals; Data protection -- Periodicals; Privacy, Right of -- Periodicals; 005.805
• Languages: English
• ISSNs: 1741-8496

Abstract

Android's large market share makes it attractive for malicious apps, which continue to exist in online markets despite vetting procedures. To alleviate this issue several malware detection methods have been developed-based primarily on the requested permissions and other components that could be extracted from the AndroidManifest.xml file. However, very few of them referred to or thoroughly analysed Android broadcast receivers (ABR). In this paper, we extensively study ABR for characterising and detecting malicious Android applications. Namely, we investigate their use patterns, study their correlations with permissions, and build prediction models with various scenarios and subset of features. Our findings show that ABR are being heavily utilised by malware apps, and are also strongly correlated with permissions. Additionally, combining ABR with permissions improves the accuracy of our prediction models and an optimal subset (~22%) of this combination achieves the same performance which further makes is less computationally demanding.