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Domain-Based Fuzzing for Supervised Learning of Anomaly Detection in Cyber-Physical Systems. In IEEE\/ACM ICSEW.  Herman Wijaya et al. 2020. Domain-Based Fuzzing for Supervised Learning of Anomaly Detection in Cyber-Physical Systems. In IEEE\/ACM ICSEW.","DOI":"10.1145\/3387940.3391486"},{"key":"e_1_3_2_2_59_1","volume-title":"Poster: Facilitating Protocol-independent Industrial Intrusion Detection Systems. In ACM CCS.","author":"Konrad Wolsing","year":"2020","unstructured":"Konrad Wolsing et al. 2020 . Poster: Facilitating Protocol-independent Industrial Intrusion Detection Systems. In ACM CCS. Konrad Wolsing et al. 2020. Poster: Facilitating Protocol-independent Industrial Intrusion Detection Systems. In ACM CCS."},{"key":"e_1_3_2_2_60_1","volume-title":"IPAL: Breaking up Silos of Protocol-dependent and Domain-specific Industrial Intrusion Detection Systems. arXiv:2111.03438.","author":"Konrad Wolsing","year":"2021","unstructured":"Konrad Wolsing et al. 2021 . 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