A Review of Ddos Attack Detection in Iot Networks

Abstract

The security risks and vulnerabilities associated with these resource-constrained Internet of Things (IoT) devices grow as their usability rises. Distributed Denial of Service is one of the main dangers to Internet of Things devices (DDoS). Continuous monitoring, early detection, and adaptive decision-making are necessary for IoT device security to be robust and effective. With software-defined networking (SDN), these issues can be solved, giving IoT devices the chance to manage DDoS threats in an efficient manner. This study suggests a unique SDN-based secure IoT framework that uses IP Payload Analysis and session IP counters to identify IoT device vulnerabilities and malicious traffic sent by IoT devices. The proposed methods can readily identify the DDoS attack in the SD-IoT network by analyzing several metrics, even with high traffic volumes, thanks to the DDoS attack detection module of the framework. By creating a lot of traffic from a compromised node, which is later identified and alerted, these tactics are tested on an SDN controller. The results and comparison analysis show that the suggested framework effectively and accurately detects DDoS attacks in their early stages, with a detection rate ranging from 98% to 100% and a low false-positive rate. Keywords; IoT networks, DDoS, DDoS attacks, DDoS Detection and IoT security