Optimized Deep Learning for Anomalous Traffic Classification in Resource-Constrained IoT Networks: A Novel Approach

AI-based traffic classification is crucial for optimizing resource allocation and threat detection in networks, particularly in resource-constrained IoT environments where traditional methods are inadequate. However, deploying large machine learning (ML) and deep learning (DL) models in such environments poses significant challenges due to their high computational requirements and storage needs, which can lead to inefficiencies and scalability issues. This paper introduces novel model optimization techniques-Min-BitNet and SlimMatrixNet-to overcome these limitations and replace state-of-the-art TinyML-based approaches. These models significantly reduce size while maintaining high accuracy: Min-BitNet achieves 99.46% and SlimMatrixNet achieves $\mathbf{9 6. 3 7 \%}$, compared to $\mathbf{9 9. 9 6 \%}$ for the uncompressed model. Validated through OMNeT++ simulations and hardware implementations, SlimMatrixNet offers superior performance in accuracy, size, and delay, providing an efficient IoT anomaly detection solution without compromising accuracy.