Weiterentwicklung und Evaluation eines adversarial robusten Network Intrusion Detection Systems

The thesis advances the adversarially robust network intrusion detection system Apollon by integrating additional machine‑learning classifiers and by replacing its original multi‑armed‑bandit selector with alternative heuristics such as epsilon‑greedy and Thompson‑sampling. The enhanced system is evaluated on the CIC‑IDS‑2017 dataset, where the expanded model pool markedly improves detection accuracy and resilience against black‑box adversarial attacks, and the new heuristics provide occasional robustness gains.