Multimodal information fusion for android malware detection using lazy learning

• Published in: Multimedia tools and applications Vol. 81; no. 9; pp. 12077 - 12091
• Main Authors: Qaisar, Zahid Hussain, Li, Ruixuan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: 1177: Advances in Deep Learning for Multimodal Fusion and Alignment; Computer Communication Networks; Computer Science; Data integration; Data Structures and Information Theory; Malware; Multimedia Information Systems; Special Purpose and Application-Based Systems; Android security; Lazy learning; Malware detection; Multimodal fusion; Case-based reasoning
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-10749-8

Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the provision of quality services. In this paper, multimodal analysis of malware apps has been presented. We exploit static, dynamic, and visual features of apps to predict the malicious apps using information fusion. The proposed study applies case-based reasoning; for catalyzing the process of training and validation over renowned datasets with enriched feature-set. Our proposed semi-supervised technique uses benign and malicious apps to predict and classify malware. The prediction process uses a hybrid analysis of malware. The proposed approach, due to the efficient and adaptive nature of CBR, outperforms prevalent approaches. Our approach has an accuracy of 95% and reduced rate of false negative rate and a better precision metric, which beat the state-of-the-art techniques.