Enhanced SVM Model with Orthogonal Learning Chaotic Grey Wolf Optimization for Cybersecurity Intrusion Detection in Agriculture 4.0

Abstract

Smart agriculture, also known as Agriculture 4.0, integrates cutting-edge technology with conventional farming practices through the agricultural Internet of Things (IoT). Despite its numerous advantages, Agriculture 4.0 introduces additional cybersecurity risks due to the widespread deployment of IoT-based devices. One significant threat is Distributed Denial of Service (DDoS) attacks, which can compromise the availability and integrity of agricultural systems. This paper proposes an Enhanced Multiclass Support Vector Machine (EMSVM) model for detecting DDoS attacks in Agriculture 4.0. To improve classification accuracy, the EMSVM model incorporates a novel optimization method called Orthogonal Learning Chaotic Grey Wolf Optimization (OLCGWO) for parameter selection. The performance of the proposed methodology is evaluated using two real-world traffic datasets, CIC-DDoS2019 and TON_IoT, which contain various DDoS attack scenarios. The results demonstrate the effectiveness of the EMSVM model in both binary and multiclass classification contexts.