A novel video anomaly detection using hybrid sand cat Swarm optimization with backpropagation neural network by UCSD Ped 1 dataset

Abstract

Abnormal symptom detection can reduce the operating costs of power companies, human resource costs, and improve the quality of power grid services. Nonetheless, intrusion detection systems encounter dimensionality problems as next-generation communication networks become increasingly varied and linked. In this study, a backpropagation neural algorithm (BP) is proposed for a Sand Cat Swarm Optimization (SCSO) electrical inspection anomaly detection model. Using genetic algorithms (GA) for feature selection and slope reduction optimization, a novel intrusion detection model was proposed. To substantially enhance the detection capabilities of the proposed model, we initially employed a genetic algorithm-based approach to select highly relevant feature sets from the UCSD Ped 1 dataset. Subsequently, we utilized the SCSO method to train a backpropagation neural network (BPNN). The hybrid SCSO-BPNN approach was employed to address binary and multiclass classification challenges using the UCSD Ped 1 dataset. The effectiveness of the Hybrid Sand Cat Swarm Optimization algorithm and backpropagation model was validated through the application of both the UCSD Ped1 and KDD 99 datasets. In the proposed hybrid algorithm, the SCSO operator plays a crucial role in detecting the globally optimal solution, while simultaneously preventing the BP from becoming trapped in a local optimum. The evaluation of the algorithm effectiveness revealed that SCSO exhibited superior performance compared to PSO, GWO, and GA in terms of both solution quality and consistency. SCSO was employed to identify the optimal weights and thresholds for BP. The proposed model was validated using the electrical data supplied by utility companies during the experimental stage. The findings indicate that the SCSO-BP algorithm consistently achieves a decision-making precision exceeding 99.75%. This algorithm proved to be well suited for power grid surveillance and outperformed other algorithms in terms of accuracy.