Intrusion detection system extended CNN and artificial bee colony optimization in wireless sensor networks

Wireless Sensor Network (WSN) communication encounters security vulnerabilities, particularly with network traffic being susceptible to attacks during routing. The effective use of Deep Learning (DL) methods has been demonstrated in developing Intrusion Detection Systems (IDSs) to manage security attacks in Wireless Sensor Networks (WSN). Consequently, the development of new IDS becomes imperative, with DL and optimization algorithms offering superior attack detection capabilities. To address this need, we propose one new IDS by integrating Fuzzy Temporal rules and Artificial Bee Colony (ABC) optimization algorithm with Convolutional Neural Network (CNN) optimized with (FT-ABC-CNN) to enhance the classifier performance. To assess its effectiveness, a comparative analysis was conducted between the newly proposed FT-ABC-CNN algorithm and other classification algorithms commonly employed in Intrusion Detection System design, such as CNN, Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). Experimental evaluations revealed that the FT-ABC-CNN algorithm surpassed these comparable classifiers in terms of accuracy enhancement and reduction in false positive rates.