Android Malware Detection Using ML: Comparison
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WitTh the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of is systematic review discussed ML-based Android malware detection techniques. It critically evaluatesd 106 carefully selected articles and highlightsed their strengths and weaknesses as well as potential improvements. Finally, tThe ML-based methods for detecting source code vulnerabilities arewere also discussed, because it might be more difficult to add security after the app is deployed. Therefore, this entry aimspaper aimed to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

  • Android security
  • malware detection
  • code vulnerability
  • machine learning
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