Robustness as a Tunable Design Objective for Lightweight IoT Intrusion Detection

Abstract

Deep learning intrusion detectors for IoT networks are mostly evaluated on clean-data accuracy alone, leaving their behavior under perturbation unknown. This paper treats robustness as a tunable design parameter rather than an uncontrolled byproduct. The mechanism itself is not novel, since training with additive input noise penalizes the norm of the input gradient. What is new is its quantified use as an operational control, where one training-time parameter sets a measurable robustness radius. On ToN-IoT and Edge-IIoTset, we evaluate a lightweight CNN-BiLSTM-Attention detector of about 200 K parameters with mutual-information feature selection. Under test-time Gaussian noise and projected gradient descent on modifiable continuous features, clean-data accuracy does not guarantee stability after deployment. A strong random forest, a high-accuracy deep model and a parameter-matched transformer all collapse under small perturbations, so the fragility belongs to the detection task rather than to any model family. Noise-aware training establishes a controllable radius matched to the expected deployment perturbation, retaining about 88 percent accuracy where baselines collapse. It costs clean-data attack recall, which a validation-calibrated threshold restores without reducing robustness, and gradient-free attacks confirm the robustness is genuine. Robustness should therefore be reported alongside accuracy and efficiency as a standard axis for IoT intrusion detection.