Hybrid PSO-Bagging Approach for Efficient and Accurate Network Anomaly Detection

The surge in internet usage has triggered a substantial increase in network attacks, raising serious cyber security concerns. Fog computing, which enhances cloud computing by providing low-latency services to mobile users, is particularly susceptible to these threats due to its proximity to end users and limited computational resources. Traditional Intrusion Detection Systems (IDS) designed for conventional networks may not directly apply to fog computing environments, where the ability to process and analyze large volumes of data efficiently is crucial. This paper presents a novel approach for network anomaly detection within fog environments, utilizing a Particle Swarm Optimization (PSO) -based Wrapper feature selection method combined with the Bagging technique. By applying this methodology to the NSL-KDD dataset, our approach effectively reduces computational complexity and improves the accuracy of intrusion detection models. The proposed system demonstrates superior performance compared to existing methods, achieving an impressive 98.3% accuracy and a low false positive rate of 1.5%. These results underscore the potential of the PSO-Bagging framework to enhance the security of fog computing systems, offering a robust solution to the growing problem of network attacks in distributed computing environments.