Probability density-based quantized spiking neural network for efficient intrusion detection in mobile ad hoc networks

Abstract

Mobile ad hoc networks (MANETs) enable infrastructure-free wireless communication through decentralized and self-organizing network architectures, making them well suited for dynamic environments such as disaster recovery, military operations, and intelligent transportation systems. However, their distributed topology and continuously changing network structure expose them to sophisticated routing attacks, particularly blackhole attacks (BHA) and wormhole attacks (WHA), which significantly degrade network reliability and challenge conventional intrusion detection techniques. Existing deep learning approaches often struggle to accurately distinguish malicious from legitimate traffic because of high-dimensional feature spaces and limited computational resources in MANET environments. To address these challenges, this paper proposes a probability density-based quantized spiking neural network (PDF-QSNN) framework for efficient intrusion detection in MANETs. The proposed framework first employs adaptive moth flame optimization (AMFO) to identify the most informative features, thereby reducing data redundancy and computational complexity. Subsequently, probability density-based feature encoding generates a discriminative probabilistic representation of network behavior, while the quantized spiking neural network performs lightweight and energy-efficient intrusion classification. Experimental evaluations using simulated BHA and WHA datasets demonstrate that the proposed framework achieves classification accuracies of 92.86% and 91.56%, respectively, outperforming conventional convolutional neural networks (CNN) and stacked recurrent long short-term memory (SRLSTM) models. These results demonstrate the effectiveness of the proposed framework for accurate and computationally efficient intrusion detection in resource constrained MANET environments.