Empowering IoT networks
Mohammad Zahid, Taran Singh Bharati · 2024
The Internet of Things (IoT) has revolutionized cybersecurity by connecting billions of objects to the Internet, but it also presents a significant cybersecurity risk. Large IoT endpoints can be exploited by malicious actors, leading to potential distributed denial of service (DDoS) attacks. Traditional security techniques, such as signature-based intrusion detection systems, have limitations, such as human judgment and insufficient ability to handle novel threats like zero-day attacks. Machine learning and deep learning techniques have emerged as a solution, enabling them to identify new threats and adapt to evolving attack strategies. This chapter reviews the advancements in machine learning and deep learning techniques for detecting DDoS attacks in IoT. Researchers interested in IoT security will also benefit from this chapter’s addition to their expertise.