Proceedings of International Conference on Applied Innovation in IT
2025/12/22, Volume 13, Issue 5, pp.737-746

Anomaly-Aware Deep Learning for DDos Detection with Optimization and Knowledge Distillation


Riyadh Rahef Nuiaa Alogaili, Saif Ali Abdulhussein, Ali Abdukadhim Taher, Dhiah Al-Shammary, Ayman Ibaida and Selvakumar Manickam


Abstract: Distributed Denial of Service (DDoS) attacks continue to overwhelm networked systems, demanding detectors that are accurate, low-false-alarm, transferable, and deployable. We propose OSES-DL, an Optimization-guided Statistical Ensemble Synergistic Deep Learning framework that advances all four fronts. The method introduces: (i) an Optimization-driven Feature Evolution Layer (OFEL) that co-trains feature sparsity with accuracy, stability, and entropy preservation; (ii) a Statistical Deep Synergy Module (SDSM) that injects Mahalanobis anomaly priors directly into BiLSTM hidden states, yielding anomaly-aware representations; (iii) Ensemble Knowledge Distillation with class-conditional temperature and feature–logit coupling (EKD-CCT) for calibrated, lightweight deployment; and (iv) a Cross-Domain Generalization Regularizer (CDGR) that combines prior-weighted MMD and CORAL for layer wise domain alignment. On CICDDoS2019, OSES-DL attains 99.45% accuracy, F1 0.994, AUC 0.998, and FAR 0.62%, with ECE 0.9%. Trained on CICDDoS2019 and tested on UNSW-NB15 and CAIDA, it improves F1 by +1.0% and reduces FAR by 0.5%–0.6% over the strongest baseline, while maintaining near-BiLSTM latency. Leave-one-attack-type-out tests confirm robustness to unseen vectors. Ablations attribute FAR reduction to SDSM, calibration to OFEL/EKD, and transferability to CDGR. OSES-DL delivers a principled, operationally grounded detector that is both state-of-the-art and deployment-ready.

Keywords: DDoS Detection, Intrusion Detection Systems, Optimization-Guided Feature Evolution, Knowledge Distillation, Domain Generalization.

DOI: Under indexing

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