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Machine Learning and Visualization tools for Cyberattack Detection

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Abstract

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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Dates and versions

hal-03647627 , version 1 (20-04-2022)

Identifiers

  • HAL Id : hal-03647627 , version 1

Cite

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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