Event classification using improved salp swarm algorithm based probabilistic neural network in fiber-optic perimeter intrusion detection system

Abstract To remarkably upgrade the performance of the intrusion detection system based on single-mode—multimode—single-mode (SMS) fiber structure which can effectively discriminate man-made event and natural event in zone perimeter, a novel event classification approach is presented in this paper. Firstly, the original intrusion signal is decomposed by the filter bank into four frequency channels with an interval of 11745 Hz. Then, the singular value and kurtosis extracted from each frequency channel as combinations of eigenvectors, being transferred to the probabilistic neural network (PNN) for training and classification. Finally, the weight factor and adaptive mutation operator are embedded in the salp swarm algorithm (SSA), which is utilized to optimize and search for the optimal smoothing factor of the PNN classifier and compared with the other four metaheuristic (MH) algorithms under five algorithm performance evaluation metrics. Large practical experiments exhibit that the presented scheme can accurately recognize man-made events (knocking, rattling) and natural events (wind, rain) respectively, shedding light on the feasibility and applicability of SMS fiber structure in zone perimeter.