Malware detection in Android IoT systems using deep learning

Waqar, Muhammad, Fareed, Sabeeh, Kim, Ajung, Ur Rehman Malik, Saif, Imran, Muhammad and Usman Yaseen, Muhammad (2022) Malware detection in Android IoT systems using deep learning. Computers, Materials & Continua, 74 (2). pp. 4399-4415. ISSN 1546-2226

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The Android Operating System (AOS) has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things (IoT). Due to the high popularity and reliability of AOS for IoT, it is a target of many cyber-attacks which can cause compromise of privacy, financial loss, data integrity, unauthorized access, denial of services and so on. The Android-based IoT (AIoT) devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market. Recently, several detection preventive malwares are developed to conceal their malicious activities from analysis tools. Hence, conventional malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the AIoT. In this paper, we proposed the novel deep learning-based real-time multiclass malware detection techniques for the AIoT using dynamic analysis. The results show that the proposed technique outperforms existing malware detection techniques and achieves detection accuracy up to 99.87%.

Item Type: Article
Uncontrolled Keywords: android, cyber security, deep learning, internet of things, malware detection
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Health & Science > Department of Science & Technology
Depositing User: David Upson-Dale
Date Deposited: 29 Apr 2024 09:38
Last Modified: 29 Apr 2024 09:38

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