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Conference papers

Machine Learning and Visualization tools for Cyberattack Detection

Robin Duraz 1, 2, 3 David Espes 4, 5 Julien Francq 6 Sandrine Vaton 3, 2
2 Lab-STICC_MATHNET - Equipe Math & Net
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
4 Lab-STICC_IRIS - Equipe SecurIty and Resilience of Information Systems
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Résumé : As technology develops and pervades our world, IT threats are becoming more and more common. Cyberattacks, while relatively rare a decade ago, are nowadays occurring much more frequently, putting at risk various institutions, ranging from a simple hospital to big companies. While it is necessary to secure a system, attackers are always finding new ways to circumvent security measures, thus motivating the use of Intrusion Detection Systems (IDS) to detect cyberattacks. In this work, results obtained by using Machine Learning (ML) algorithms to detect cyberattacks in a public dataset, and visualization tools that can provide a subjective assessment of the task difficulty and the ML model quality are presented.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-03647627
Contributor : Sandrine Vaton Connect in order to contact the contributor
Submitted on : Wednesday, April 20, 2022 - 4:47:30 PM
Last modification on : Sunday, April 24, 2022 - 3:28:39 AM

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Ressi2022_RobinDURAZ.pdf
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  • HAL Id : hal-03647627, version 1

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Robin Duraz, David Espes, Julien Francq, Sandrine Vaton. Machine Learning and Visualization tools for Cyberattack Detection. RESSI 2022 : Rendez-vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information, May 2022, Chambon-sur-Lac, France. ⟨hal-03647627⟩

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