Toward Adversarial Robust and Edge-Deployable Network Intrusion Detection: An Attention-Augmented Architecture with Data Validation

Hybrid deep-learning Network Intrusion Detection Systems (NIDS) achieve strong clean-data accuracy but typically leave adversarial robustness, deployment cost, and quantitatively verified explainability unaddressed. We formulate NIDS design as a constrained multi-objective learning problem spanning detection accuracy, adversarial robustness, latency/resource-bounded deployability, and explanation fidelity, and we design a corresponding attention-augmented architecture with SHAP explainability, FGSM/PGD adversarial training, and knowledgedistilled edge deployment. To validate the overall pipeline before a full-scale implementation on real benchmark traffic, we build, from scratch in NumPy, a lightweight prototype (per-timestep feature projection, a recurrent temporal encoder, single-head self-attention pooling, and a softmax classifier, trained via manually-derived backpropagation-through-time) and evaluate it end-to-end on a controlled synthetic multi-class flow-sequence dataset. The prototype reaches 94.7% test accuracy (macro-F1 0.947, AUC 0.970) on held-out synthetic data, degrades from 94.7% to 80.3% accuracy under an undefended FGSM attack as the perturbation budget grows, and a 9.2x smaller variant matches its accuracy while cutting measured inference latency by 2.5x. These genuine, reproducible findings validate the pipeline and concretely motivate the adversarial-hardening and distillation components of the full design, whose evaluation on real, large-scale NIDS benchmarks is identified as the next phase of this research.