Browsing by Author "Salmi, Cheikh"

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    Confluence et préservation de la propriété de normalisation forte du système @
    (2006) Salmi, Cheikh
    Le @ calcul est une extension simple du -calcul classique qui n’utilise aucun codage supplémentaire pour désigner les termes. Dans ce mémoire nous nous sommes principalement intéressé à l’étude de la confluence et la préservation de normalisation forte du @ calcul. En constatant, à travers notre étude des calculs avec substitution explicite, que la confluence et la préservation de la normalisation forte des calculs ne sont pas des propriétés évidentes à établir directement, nous avons considéré le @ en le présentant comme un formalisme de réécriture d’ordre supérieur. Ainsi, nous avons prouvé la normalisation forte du @ en utilisant une combinaison des techniques du ‘semantic labelling’ et l’ordre récursif sur les chemin. La confluence faible du @ est évidente après l’étude et la résolution de ses paires critiques.La confluence du @ découle immédiatement de sa préservation de normalisation forte et sa confluence faible par le lemme de Newman. La simplicité des règles de réécriture du @ calcul rend intéressant la définition d’une machine efficace exécutant les réductions de ce calcul. Il serait aussi intéressant de définir une version typée pour calcul
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    A Parallel Heuristic Scheduler for Cloud Computing Environment
    (IEEE, 2021) Salmi, Cheikh; Walker, Jessie; Ait Bouziad, Ahmed
    Cloud computing is a different paradigm from traditional computing. It consists of providing IT services such as servers, storage, databases, network management, software, data analytics, artificial intelligence, etc., via the Internet (the Cloud) to provide faster innovation, flexible resources, productivity, and competitiveness. Several challenges in handling end-user applications need to be addressed more efficiently. Task Scheduling is a significant problem in Cloud computing since the cloud provider has to deal with many user applications. Consequently, task scheduling can no longer be handled by traditional schedulers. This paper's primary purpose is to propose a parallel multi-core hybrid heuristic scheduler based on exceeding the computing capacity of any processor while guaranteeing the results' accuracy. The main objective is to determine the feasible schedule that minimizes applications execution time while maximizing cloud resource utilization. Tests on benchmark instances showed that the proposed approach finds optimum/near-optimum solutions in many cases, while the computational times are minimal compared to other sequential techniques found in the literature
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    Transformer-Based Approach for Intrusion Detection System
    (Institute of Electrical and Electronics, 2025) Senoussi, Nour El Houda; Salmi, Cheikh; Banouh, Nassim; Khalfi, Adem
    Intrusion Detection Systems (IDS) have been used for years to protect enterprise hosts from cyberattacks. Traditional IDSs are usually based on simple methods, such as signatures or heuristics, that do not adapt to reactions against new threats that are constantly increasing. The objective of this paper is to develop an IDS based on a deep learning technique which is transformers. Unlike conventional models and thanks to their self-attention mechanism, transformers are characterized by an excellent ability to support complex patterns by very accurately modeling the context in sequential data. A host-based dataset containing system logs and network activities is used to train the transformer model that forms the core of the developed IDS. A detailed evaluation is used to compare our approach against existing methods based on machine learning and deep learning, showing significant improvements in precision, recall, and false positive rate. These results are very encouraging for developing robust IDSs that can be fine-tuned in real time to take into account new attacks