A real-time, collaborative host-based intrusion detection system leveraging bigbird embeddings and Deep q-learning / Zakaria Remmache

Public ISBD Titre : A real-time, collaborative host-based intrusion detection system leveraging bigbird embeddings and Deep q-learning Type de document : document multimédia Auteurs : Zakaria Remmache, Auteur ; Tahar Hachani, Auteur ; Taher Allaoui, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 66 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Data science and artificial intelligence Langues : Anglais Mots-clés : Cybersecurity  Real time  System Logs  Linux  Artificial intelligence Résumé : This dissertation presents a smart system for detecting cyber-attacks on computers by analyzing system activity in real time. The system focuses on information collected from Linux operating systems and uses recent advances in artificial intelligence to identify suspicious behavior. It processes system logs, transforms them into a form that a machine can understand, and uses a learning agent to decide whether the activity is normal or potentially harmful. The system improves over time by learning from its own experience. It is designed to respond quickly to threats while minimizing false alarms, and it can be deployed across different machines to share knowledge. Overall, this work aims to provide a modern and adaptive solution for enhancing computer security. note de thèses : Mémoire de master en informatique A real-time, collaborative host-based intrusion detection system leveraging bigbird embeddings and Deep q-learning [document multimédia] / Zakaria Remmache, Auteur ; Tahar Hachani, Auteur ; Taher Allaoui, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 66 p. + 1 disque optique numérique (CD-ROM). Option : Data science and artificial intelligence Langues : Anglais Mots-clés : Cybersecurity  Real time  System Logs  Linux  Artificial intelligence Résumé : This dissertation presents a smart system for detecting cyber-attacks on computers by analyzing system activity in real time. The system focuses on information collected from Linux operating systems and uses recent advances in artificial intelligence to identify suspicious behavior. It processes system logs, transforms them into a form that a machine can understand, and uses a learning agent to decide whether the activity is normal or potentially harmful. The system improves over time by learning from its own experience. It is designed to respond quickly to threats while minimizing false alarms, and it can be deployed across different machines to share knowledge. Overall, this work aims to provide a modern and adaptive solution for enhancing computer security. note de thèses : Mémoire de master en informatique

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