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Bayesian Classifiers in Intrusion Detection Systems2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.379-391, ⟨10.1007/978-3-030-45778-5_26⟩
Conference papers
hal-03266456v1
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How Dangerous Is Internet Scanning?7th Workshop on Traffic Monitoring and Analysis (TMA), Apr 2015, Barcelona, Spain. pp.158-172, ⟨10.1007/978-3-319-17172-2_11⟩
Conference papers
hal-01411192v1
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Towards a Cooperative Intrusion Detection System for Cognitive Radio NetworksInternational IFIP TC 6 Workshops PE-CRN, NC-Pro, WCNS, and SUNSET 2011 Held at NETWORKING 2011 (NETWORKING), May 2011, Valencia, Spain. pp.231-242, ⟨10.1007/978-3-642-23041-7_22⟩
Conference papers
hal-01587836v1
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Intrusion Detection in the Smart Grid Based on an Analogue TechniqueInternational Workshop on Open Problems in Network Security (iNetSec), Oct 2015, Zurich, Switzerland. pp.56-67, ⟨10.1007/978-3-319-39028-4_5⟩
Conference papers
hal-01445793v1
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Machine learning methods for anomaly detection in IoT networks, with illustrationsBoumerdassi S., Renault É., Mühlethaler P. (eds), Machine Learning for Networking, Lecture Notes in Computer Science, vol 12081. Springer, pp.287-295, 2020, ⟨10.1007/978-3-030-45778-5_19⟩
Book sections
hal-02977813v1
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A Game of Microservices: Automated Intrusion Response18th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), Jun 2018, Madrid, Spain. pp.169-177, ⟨10.1007/978-3-319-93767-0_12⟩
Conference papers
hal-01824638v1
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Arguments Against Using the 1998 DARPA Dataset for Cloud IDS Design and Evaluation and Some Alternative2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.315-332, ⟨10.1007/978-3-030-45778-5_21⟩
Conference papers
hal-03266464v1
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