IoT-based WISNE-SDN detection and DDOS attack mitigation using machine learning techniques
Year: 2026
Type: article
Abstract: The Internet of Things (IoT) refers to a system of interconnected computing devices, sensors, and supporting infrastructure. Attacks from Distributed Denial of Service (DDoS) and insufficient resources are common issues for this network. Security and access control might be enhanced by integrating the IoT with Software-Defined Networking (SDN). A method for detecting DDoS attacks in Wireless Sensor Networks (WISNE) using machine learning (ML) is discussed in this article. The WISNE-SDN IoT controllers could make use of this technique. In a testbed environment that mimics DDoS attack traffic, the WISNE-SDN controller may gather network events into a pre-processed dataset. For tasks like packet sorting and attack detection, the framework employs some ML algorithms, such as K-Nearest Neighbor (KNN), XGBoost (XGB), and Naive Bayes (NB). Accuracy levels of 97% for KNN, 100% for XGB. The suggested approach improves the security and stability of IoT networks in SDN-IoT environments by making them more resistant to DDoS attacks.
Source: PeerJ Computer Science
Authors: N. Raghavendran, Y. Harold Robinson
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,395
OpenAlex ID: W7131361647