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PAIRED: An Explaina...
PAIRED: An Explainable Lightweight Android Malware Detection System
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- Alani, Mohammed M. (författare)
- Department of Computer Science, Toronto Metropolitan University, Toronto, ON, Canada; School of IT Administration and Security, Seneca College of Applied Arts and Technology, Toronto, ON M2J 2X5, Canada
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- Awad, Ali Ismail (författare)
- Luleå tekniska universitet,Digitala tjänster och system,College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates; Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Qena 83513, Egypt; Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, U.K.
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 73214-73228
- Relaterad länk:
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https://doi.org/10.1...
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- With approximately 2 billion active devices, the Android operating system tops all other operating systems in terms of the number of devices using it. Android has gained wide popularity not only as a smartphone operating system, but also as an operating system for vehicles, tablets, smart appliances, and Internet of Things devices. Consequently, security challenges have arisen with the rapid adoption of the Android operating system. Thousands of malicious applications have been created and are being downloaded by unsuspecting users. This paper presents a lightweight Android malware detection system based on explainable machine learning. The proposed system uses the features extracted from applications to identify malicious and benign malware. The proposed system is tested, showing an accuracy exceeding 98% while maintaining its small footprint on the device. In addition, the classifier model is explained using Shapley Additive Explanation (SHAP) values.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Nyckelord
- Android
- malware
- malware detection
- XAI
- machine learning
- Information systems
- Informationssystem
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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