With 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 ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements. Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions. With the swift adoption of Android devices all over the world, it has become a major target for attacks from malware which brings great danger to both users and organizations. With the growing complexity of modern malware, traditional methods of signature-based malware detection are painfully sluggish. It is for this reason that there is recent notice towards the use of ML in the classification and detection of malware.This systematic review monitors new developments in the detection of Android malware through the use of machine learning techniques. We cover different methodologies, such as static, dynamic, and hybrid analysis, reviewing their advantages and disadvantages. We also focus on research feature extraction methods, classification algorithms, and available datasets. Adverse challenges such as adversarial dataset, model quality, and interpretability are discussed as well.Our results outline the effectiveness of deep learning models, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for the purposes of malware detection. Even so, high false positive rates and overhead computation are still open challenges. The review documents the most recent developments, determines under-researched areas, and proposes ways of improving the literature to aid in the development of Android malware detection systems with machine learning techniques.
Android Security, Malware Detection, Code Vulnerability, CNN
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