Advanced Android Malware Detection Utilizing API Calls and Permissions

Chowdhury, Md Naseef-Ur-Rahman; Alahy, Qudrat E.; Soliman, Hamdy

doi:10.1007/978-981-16-4118-3_12

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Chowdhury, Q.E. Alahy, and H. Soliman proposed a method for detecting Android malware [14]. The proposed approach involves creating a feature vector based on API calls and permissions, which are then used to train a machine learning classifier.…”

Section: Comparison Of the Approachesmentioning

confidence: 99%

“…2. List of sensitive APIs [14] The static analysis involves extracting features from the Android Manifest and the Dalvik Bytecode, while the dynamic analysis involves capturing system calls and network behavior. The dataset used for evaluation consists of over 10,000 Android applications, of which 5,000 are benign and 5,000 are malicious.…”

Section: Comparison Of the Approachesmentioning

confidence: 99%

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Android malware Detection using Machine learning: A Review

Chowdhury¹,

Haque²,

Soliman³

et al. 2023

Preprint

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<p>Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection using machine learning in this paper. We begin by providing an overview of Android malware and the security issues it causes. Then, we look at the various supervised, unsupervised, and deep learning machine learning approaches that have been utilized for Android malware detection. Additionally, we present a comparison of the performance of various Android malware detection methods and talk about the performance evaluation metrics that are utilized to evaluate their efficacy. Finally, we draw attention to the drawbacks and difficulties of the methods that are currently in use and suggest possible future directions for research in this area. In addition to providing insights into the current state of Android malware detection using machine learning, our review provides a comprehensive overview of the subject.</p>

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Section: Comparison Of the Approachesmentioning

confidence: 99%

Section: Comparison Of the Approachesmentioning

confidence: 99%