An Analysis of Machine Learning-Based Android Malware Detection Approaches. Issue 1 (1st August 2022)

• Record Type: Journal Article
• Authors: Srinivasan, R.; Karpagam, S; Kavitha, M.; Kavitha, R.
• Is Part Of: Journal of physics. Volume 2325:Issue 1(2022)
• Publication Date: 2022-08-01
• Subjects: Malcode -- Malware -- Android Security -- Machine learning -- Feature extraction; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2325/1/012058
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Despite the fact that Android apps are rapidly expanding throughout the mobile ecosystem, Android malware continues to emerge. Malware operations are on the rise, particularly on Android phones, it make up 72.2 percent of all smartphone sales. Credential theft, eavesdropping, and malicious advertising are just some of the ways used by hackers to attack cell phones. Many researchers have looked into Android malware detection from various perspectives and presented hypothesis and methodologies. Machine learning (ML)-based techniques have demonstrated to be effective in identifying these attacks because they can build a classifier from a set of training cases, eliminating the need for explicit signature definition in malware detection. This paper provided a detailed examination of machine-learning-based Android malware detection approaches. According to present research, machine learning and genetic algorithms are in identifying Android malware, this is a powerful and promising solution. In this quick study of Android apps, we go through the Android system architecture, security mechanisms, and malware categorization.