Malware Analysis for IoT and Smart AI-Based Applications

One of the major challenges in today’s world of the Internet of Things (IoT) and smart applications is the wide deployment of millions of devices that require a reliable and secure communication structure. The enormous growth in wireless communication and the development of 5G brings an opportunity for many emerging IoT and smart AI-based applications. These networks have increased traffic and increased the level of complexity. It brings many new challenges when numerous IoT and smart devices are connected and start sharing data. These communications are vulnerable to security threats, malicious users/intruders, and malware. Malware is malicious software that can slow a system down, hang often, steal important data, and otherwise interfere with operations. In order to adapt automatically and dynamically to both unintentional and intentional flaws and attacks, IoT networks and smart AI-based application integration must be created while keeping such hazards in mind. To develop such solutions, a detailed understanding of malware threats in this context is essential. To some extent, many proposed studies have been proposed to serve the IoT malware detection domain. Therefore, in this chapter, we systematically examine the different kinds of possible malware and taxonomy. A comprehensive survey of the latest developments and state-of-the-art malware detection approaches is presented. Additionally, we present a detailed analysis of IoT and smart AI-based applications malware evasion techniques. Finally, the chapter aims to provide a comprehensive understanding of IoT malware ecosystems and can aid in developing future defenses.