Mutation Pelican Optimization Algorithm (MPOA) Based Feature Selection and DCNN Model For Traffic Anomaly Detection

The detection of abnormal traffic in Wireless Sensor Networks (WSNs) is a major research problem as the complexity of the cyber-attack increases along with the limitations of available resources of the sensor nodes. In an attempt to solve the issues related to the high dimensionality of feature space and underfitting in the detection of traffic anomalies, this paper presents a unique Mutation Pelican Optimization Algorithm (MPOA) used in association with a Deep Convolutional Neural Network (DCNN). Within MPOA, the hunting behaviors of pelicans are utilized to create a population of solutions that help to determine the best feature subsets. In this proposed methodology, the concept of Mutual Information (MI) is employed to rank features based on the relationship between network characteristics and class labels. Then MPOA performs the selection of features before using the DCNN for classification. Finally, the selected features are provided to the DCNN, which recognizes the hierarchical features and classifies the network traffic either as normal or as DoS. The proposed framework combines lightweight network architecture with effective feature learning while operating on an optimally selected subset of features, thereby reducing computational complexity and storage requirements. The model was validated using three benchmark intrusion detection datasets, NSL-KDD, KDD Cup 99, and UNSW-NB15, and its performance was compared with several state-of-the-art machine learning and deep learning models, including DNN, CNN, SNN, and conventional DCNN. Experimental results demonstrate that the proposed MPOA–DCNN model achieves superior attack detection performance while maintaining a compact model size, making it well suited for deployment in resource-constrained WSN environments.