A Survey on Machine and Deep Learning Based Intrusion Detection Systems for IoT
A. Haripriya · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: In recent times, the Internet of Things (IoT) and its diverse range of applications have emerged as one of the most popular and highly researched areas. The characteristics that make IoT easily applicable to real-life applications also expose it to cyber threats, thereby emphasizing the need for effective security measures. The rapid advancement of IoT is revolutionizing business processes and society as a whole. However, as this technology continues to evolve, it becomes increasingly important to prioritize the detection and awareness of vulnerabilities. Adopting a proactive approach is crucial to prevent unauthorized access to critical resources and business functions, thereby ensuring the continuous availability and operation of the system. Failing to prevent the occurrence of DoS and DDoS attacks can have adverse effects on data confidentiality, integrity, and availability, thereby highlighting the importance of effective intrusion detection and prevention measures. The literature has presented a wide range of intrusion detection methods aimed at addressing computer security threats. These methods can generally be categorized into