5 papers
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
Florian Spelter, Johannes F. Loevenich, Tobias Hürten +4
Although Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) are increasingly deployed in operational networks, their dependence on large-scale training data intr…
Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defence
Tobias Hürten, Johannes F. Loevenich, Florian Spelter +5
This paper proposes an Autonomous Cyber Operations (ACO) gym, a unified framework integrating high-fidelity network emulation, realistic user behaviour modelling for benign network…
Learning to Cover Locally: Graph Neural Combinatorial Optimization under a Hard Information Horizon
Johannes F. Loevenich, Thies Moehlenhof, Laurin Holz +3
Neural combinatorial optimization typically assumes a centralized solver that reads the whole instance. We study the opposite: combinatorial optimization under a hard information h…
FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems
Maxime Schwarzer, Laurin Holz, Tobias Huerten +4
Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evas…
AI Model Extraction Attacks: Bypassing Single-Client Assumptions in Defenses
Maxime Schwarzer, Johannes F. Loevenich, Gustavo Sánchez +5
Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is essential for maintaining inform…