Intrusion and Malware Detection and Prevention Techniques in IoT
Ramanpreet Kaur, Parveen Singla, Jaskirat Kaur, B.J Santhosh Kumar · 2024
Lack of security has become a major problem and now poses a threat to the safety and security of both computer systems and stakeholders due to the surge in malware activity brought on by the swift development of technology. In order to maintain stakeholder security, particularly that of end users, one of the most pressing considerations is protecting the data from fraudulent attempts to access it. A collection of malicious programming code, scripts, active content, or intrusive software that targets trustworthy computer programmers, mobile apps, or websites is known as malware. According to a study, inexperienced users are unable to distinguish between safe and harmful software. Computer systems and mobile applications should be developed to detect dangerous activities in order to secure the stakeholders. By utilizing innovative concepts like Artificial Intelligence and with the growth of the internet, the use of Internet of Things (IoT) devices is rising tremendously. The amount of data on IoT devices is growing, making them more vulnerable to malware assaults. As a result, malware detection is a crucial concern for IoT devices. The present chapter emphasizes Artificial Intelligence (AI)-based techniques in this study for identifying and stopping malware activities. A thorough study of the flaws of the most recent malware detection methods as well as suggestions for how to make them more effective are provided. The study posits that employing futuristic methods for the creation of malware detection programs will offer important benefits. Understanding of this chapter will aid researchers in their continued study of malware identification and prevention techniques in IoT based on AI.