An Enhanced Intrusion Detection System for Wireless Sensor Networks Using Cuckoo-Optimized Neural Networks

Wireless Sensor Networks are critical from the security point of view because of their distributed nature and resource constraints. Artificial Intelligence techniques have shown promising results in intrusion detection, but their performance optimization is paramount. This paper proposes a new approach based on combining Multi-Layer Perceptron neural networks with the Cuckoo Optimization Algorithm for efficient intrusion detection in WSN. Our methodology involves three main steps: (1) data preprocessing using the k-nearest neighbor for missing value imputation and normalization, (2) reduction of dimensionality through Principal Component Analysis, reducing the features from 41 to 38 dimensions, and (3) neural network optimization using COA for weight and bias parameter tuning. Our approach has yielded an accuracy of 99.1% in intrusion detection using the NSL-KDD dataset, which shows an improvement of about 3% compared to traditional methods. The proposed system performs better in terms of detection accuracy, reduction of false alarm rate, and computational efficiency.