Print Email Facebook Twitter AGONI: Adversarial Generation Of Network Intrusions Title AGONI: Adversarial Generation Of Network Intrusions Author Thomas, Wessel (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Verwer, S.E. (mentor) Panichella, A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science | Cyber Security Date 2023-06-07 Abstract Network Intrusion Detection Systems (NIDSs) defend our computer networks against malicious network attacks. Anomaly-based NIDSs use machine learning classifiers to categorise incoming traffic. Research has shown that classifiers are vulnerable to adversarial examples, perturbed inputs that lead the classifier into misclassifying the input. Existing work has shown weaknesses in classifiers for classifying network traffic, but none have shown the possibility of automatically recreating network attacks that can bypass existing anomaly-based NIDSs. Regular methods for generating black-box adversarial examples create packets that are invalid. We present AGONI, a Multi-Objective Genetic Algorithm forgenerating network packets that are both valid packets and adversarial examples for NIDSs. AGONI can successfully generate valid adversarial examples in multiple attack scenarios. Against the NIDS Suricata, 99.93% of the generated adversarial examples can successfully bypass the defence. We compare the performance of AGONI against randomly generating network packets, the Boundary Attack and an adjusted version of the Boundary Attack which can better create valid adversarial examples. Only AGONI consistently generates valid adversarial examples when compared to the Random Attack (82%), the Boundary Attack (0%) and the Networking Boundary Attack (74%). Subject Adversarial ExamplesAnomaly DetectionNetwork Security To reference this document use: http://resolver.tudelft.nl/uuid:d31dcfab-aad4-4032-85ba-a60c723ea15e Part of collection Student theses Document type master thesis Rights © 2023 Wessel Thomas Files PDF Master_Thesis_Wessel_Thomas.pdf 4.73 MB Close viewer /islandora/object/uuid:d31dcfab-aad4-4032-85ba-a60c723ea15e/datastream/OBJ/view