Distributed denial-of-service (DDoS) attacks have become a critical threat to Internet of Things (IoT) infrastructures due to their high traffic dynamics, strong class imbalance, and strict resource constraints at the edge. This paper proposes ChebyKANRes, a lightweight intrusion detection model that combines Chebyshev polynomial expansions to parameterize learnable univariate transformations, a gate mechanism to modulate feature flow, and residual connections to stabilize optimization in deeper KAN-style stacks. Experiments were conducted on the CICIoT2023 dataset focusing on benign traffic and 12 DDoS subtypes, using a reproducible pipeline with stratified splitting, cross-validation (k = 5), and early stopping. The proposed model consistently improves multi-class performance (Accuracy: 0.9983) over an optimized MLP baseline (Accuracy: 0.9641), while maintaining a compact size suitable for edge deployment (≈123 k parameters; ~0.47 MB). Within CICIoT2023 and the evaluated split/training protocol, the proposed ChebyKANRes configuration shows improved imbalance-robust multiclass detection while maintaining a compact model size and comparable batch inference time.
Becerra-Suarez et al. (Wed,) studied this question.