Optimizing Manet Intrusion Detection Through Vae-Based Dimensionality Reduction: A Trade-Off Analysis Between Accuracy and Resource Efficiency

Mobile Ad-hoc Networks (MANETs) face unique security challenges due to their dynamic topology, distributed nature, and resource constraints. While deep learning approaches have shown promise in intrusion detection, their computa-tional overhead often conflicts with MANETs' resource limitations. This paper presents a novel hybrid architecture combining Variational Autoencoders (VAE) and one-dimensional Convolutional Neural Networks (1D-CNN) for efficient in-trusion detection in MANETs. Our approach leverages VAE for dimensionality reduction while maintaining detection accuracy through 1D-CNN classification. The proposed system first utilizes a 1D-CNN model for baseline intrusion detec-tion, reaching an accuracy of 95.79% across multiple attack types. Then, we in-corporate a VAE for feature space reduction and reduce the feature dimensionali-ty by 16.67%, while the detection accuracy is maintained at a robust value of 91.29%. Experimental results confirm that the VAE-1D-CNN hybrid architecture can provide a desirable trade-off between resource efficiency and detection per-formance. In this paper, VAE reduces a great deal of feature space with only an accuracy loss of 4.5%. Then our proposed approach has been validated on com-prehensive performance metrics: precision is 91.30%, recall is 91.29%, and F1-score is 91.27%. Confusion matrix analysis reveals that the hybrid model main-tains reliable detection capabilities with 2,562 true positives and 2,038 true nega-tives, despite a modest increase in false positives. The model's training behavior shows stable convergence and consistent validation performance, indicating ro-bust generalization capabilities. The contribution of this research is in the arena of resource-aware intrusion detection systems developed with MANET in mind. In essence, it provides a pragmatic solution to achieving both security effectiveness and computational efficiency.