Open Access

ARTICLE

Malware Detection in Android IoT Systems Using Deep Learning

by Muhammad Waqar¹, Sabeeh Fareed¹, Ajung Kim^2,*, Saif Ur Rehman Malik³, Muhammad Imran¹, Muhammad Usman Yaseen¹

1 Department of Computer Science, COMSATS University, Islamabad, 45550, Pakistan
2 Department of Optical Engineering, Sejong University, Seoul, 143747, Korea
3 Cybernetica AS, Tallinn, 12618, Estonia

* Corresponding Author: Ajung Kim. Email:

Computers, Materials & Continua 2023, 74(2), 4399-4415. https://doi.org/10.32604/cmc.2023.032984

Received 03 June 2022; Accepted 09 August 2022; Issue published 31 October 2022

Abstract

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%.

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Keywords

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