Classification of Wireless Sensor Networks and Internet of Things Attacks Based on Chimpanzee Leader Election Optimization - Convolutional Neural Networks

The vulnerability of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) to cyber attacks such as Blackhole, Flooding, Grayhole, and Time Division Multiple Access (TDMA) scheduling drives the need for accurate and resource-efficient detection solutions as one of the contributions in edge computing security. This study proposes a hybrid attack classification system by combining the Chimpanzee Leader Election Optimization (CLEO) algorithm for optimal feature selection on Convolutional Neural Networks (CNN) for attack pattern extraction. Inspired by the chimpanzee leadership hierarchy, CLEO successfully determines the optimal values of the number of neurons and the learning rate on CNN with reduced and modified datasets. CNN classifies attacks by analyzing temporal-spatial patterns of network traffic. The experimental results show that the accuracy for the training data set is 100 %, and the accuracy for the testing dataset is 98 %. This combination of CLEO-CNN not only overcomes the limitations of conventional methods in balancing accuracy and efficiency, but also offers a practical solution for real-time security on critical WSN and IoT infrastructures.