Intrusion Detection in Wireless Sensor Networks Using Multitask Residual Shrinkage CNN Optimized by Fennec Fox Algorithm

ABSTRACT Wireless sensor networks (WSNs) are generally used in environmental monitoring, data transfer, and object detection but are susceptible to intrusion attacks based on their integration with the Internet of things (IoT). Most conventional intrusion detection systems experience high false alarm rates and excessive computational overhead. For better performance in overcoming these limitations, a new intrusion detection framework called Multitask Multiattention Residual Shrinkage Convolutional Neural Network with optimization through the Fennec Fox Optimization Algorithm (MMRSCNN‐FFOA‐ID‐WSN) is proposed. The framework combines preprocessing using an Ultrawideband Nanoplasmonic Bandpass Filter (UWNPBF) to eliminate redundant and biased entries, followed by feature selection through the Binary Waterwheel Plant Optimization Algorithm (BWWPOA). The MMRSCNN combines multitask learning with channel‐wise attention and residual shrinkage operations to detect attack types from input data. To further improve detection accuracy, the weight parameters are optimized using the FFOA, chosen for its faster convergence in high‐dimensional search spaces compared to other algorithms. Experiments were performed on the WSN‐DS dataset having normal traffic and various attack types such as flooding, black hole, gray hole, and time division multiple access attacks (TDMA). The proposed method achieved an accuracy of 99.46%, specificity of 98.83%, and a false alarm rate of 1.12%, which correspond to relative improvements of up to 22.37%, 25.32%, and 32.40%, respectively, over the baseline model. These results show that MMRSCNN‐FFOA‐ID‐WSN is an efficient and energy‐saving solution for WSNs intrusion detection.