A Computationally Efficient Hybrid MLP–Fuzzy Logic Model for Blackhole Attack Detection in Mobile Ad Hoc Networks

Abstract

Mobile Ad Hoc Networks (MANETs) are infrastructure-free self-organizing networks which are dynamic in nature and as such are very vulnerable to routing attacks. The proposed paper suggests a computationally efficient hybrid model that combines a Multi-Layer Perceptron (MLP) and fuzzy inference system to identify the occurrence of blackhole attacks. Conventional detection techniques do not work well in highly dynamic MANET and identifying the blackhole attacks because of false alarms and high computational cost. The suggested hybrid model will solve this issue and meet the needs of different network traffic data conditions by integrating the MLP part, which trains intricate and non-linear associations, and fuzzy logic, which addresses uncertainty and promotes interpretability, specifically in the unclear network settings. The NSL-KDD data set, which is commonly used in the study of intrusion detection, was used to test the model. The results of the experiments prove that the proposed MLP-Fuzzy Logic model has an accuracy of 0.96, precision of 0.95, recall of 0.96, and an F1-score of 0.96 and has a false positive rate of 0.02. As the analysis shows, the application of the hybrid model is highly effective compared to single-technology applications, providing the sound and trustworthy solution to the problem of the blackhole attack detection in the MANET networks with resource limitations.