Latent States: Model-Based Machine Learning Perspectives on Cyber Resilience

Pierre-Emmanuel Arduin, Marin François and Myriam Merad
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Pierre-Emmanuel Arduin: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Marin François: LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Myriam Merad: LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: In today's world, connected systems, social networks, and mobile communications create a massive flow of data, which is prone to cyberattacks. This needs fast and accurate detection of cyber-attacks. Intelligent systems and Data analytics are important components when issues pertaining to effective security solutions become the subject of discussion. This is because there is an impending need for high volume and high velocity data from different sources to detect anomalies as soon as they are discovered. This will help reduce significantly the vulnerability of the systems as well as improve their resilience to cyber Attacks. The capability to process large volumes of information at real time through utilization of tools for data analytics has many advantages vital for analysis of cybersecurity systems. Moreover, the data collected from sophisticated intelligent systems, cloud systems, networks, sensors, computers, intrusion detection systems could be used to identify vital information. This information could

Date: 2024-09
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Published in IEEE 4th Intelligent Cybersecurity Conference (ICSC), Sep 2024, Valencia, Spain

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