Exploring Methods of Mitigation against DDoS Attack in an IoT Network
Piyush M. Prajapati, Priyesh P. Gandhi, Sheshang Degadwala
Abstract
Smart homes and industrial systems alike have benefited greatly from the increased connection and ease made possible by the explosion of Internet of Things (IoT) devices. Nevertheless, Distributed Denial of Service (DDoS) assaults have become more common due to the widespread use of these linked devices, which has put IoT networks at risk. In order to protect IoT networks against Distributed Denial of Service (DDoS) assaults, this study looks into the topic and suggests new ways to do so. The research delves into advanced tactics, such as adaptive traffic filtering, anomaly detection, machine learning algorithms, and hardware-based solutions, by examining the specific vulnerabilities of IoT ecosystems. Also covered is the possibility of using edge computing for mitigation and detection in real-time. Through simulations and experiments, the research evaluates the performance and efficacy of these methods under diverse attack scenarios, considering resource constraints and scalability. The findings not only contribute to academic discussions on IoT security but also provide practical insights for industry practitioners and policymakers. The research aims to enhance the resilience of IoT networks, ensuring the integrity, availability, and confidentiality of data in the face of evolving DDoS threats.