A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system. Issue 3 (4th March 2023)

• Record Type: Journal Article
• Authors: Gaurav, Akshat; Gupta, Brij B.; Panigrahi, Prabin Kumar
• Is Part Of: Enterprise information systems. Volume 17:Issue 3(2023)
• Publication Date: 2023-03-04
• Subjects: Network function virtualisation -- enterprise information system -- IoT -- malware detection -- adversarial malware detection -- malware visualisation techniques -- sandboxing; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2021.2023764
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: The Internet of Things (IoT) is a relatively new technology that has piqued academics' and business information systems' attention in recent years. The Internet of Things establishes a network that enables smart devices in an organisational information system to connect to one another and exchange data with the central storage. Android apps are placed on Android apps to enhance the user-friendliness of IoT devices in business information systems, making them more interactive and user-friendly. However, the usage of Android apps makes IoT devices susceptible to all forms of malware attacks, including those that attempt to hack into IoT devices and get access to sensitive information stored in the corporate information system. The researchers offered a variety of attack mitigation approaches for detecting harmful malware embedded in an Android application operating on an IoT device. In this context, machine learning offered the most promising strategies to detect malware attacks in IoT-based enterprise information systems because of its better accuracy and precision. Its capacity to adapt to new forms of malware attacks is a result of its learning capabilities. Therefore, we conduct a detailed survey, which discusses emerging machine learning algorithms for detecting malware in business information systems powered by the Internet of Things. This article reviews all available research on malware detection, including static malware detection, dynamic malware detection, promoted malware detection and hybrid malware detection.