My Education

In 2006, I completed my Baccalaureate in "Natural and Life Sciences" in Sidi Bel Abbes, Algeria. In 2009, I earned a bachelor's degree in Computer Science with a focus on "Fundamental Computer Science" from the University of Djillali Liabes, Sidi Bel Abbes. In 2011, I obtained a Master's degree in Computer Science with a specialization in "Computer Science Theory" (CST) from the same university, where I graduated at the top of my class.

In 2012, I began my doctoral thesis at the University of Oran 1 Es-Senia, focusing on "Exploring the Potential of CSP for Error Localization from Counterexamples". I was awarded my doctorate in December 2015.

In 2024, I achieved the Habilitation à Diriger des Recherches (HDR) from the École Supérieure en Informatique de Sidi Bel Abbès, Algeria.

Professional Experience

— Doctoral Student, Université d'Oran 1, 2012 - 2015
— Maître de Conférences Classe B, École Supérieure en Informatique de Sidi Bel Abbès, 2018 - 2024
— Maître de Conférences Classe A, École Supérieure en Informatique de Sidi Bel Abbès, 2024 - Present

Research Areas of Interest
  1. Applying Machine Learning in the Medical Domain

    2. The medical field generates vast amounts of complex data from diverse sources such as medical data, electronic health records, and genomic information. Traditional analytical methods often struggle to extract meaningful patterns from these high-dimensional datasets. My research focuses on applying machine learning and deep learning techniques to support medical diagnosis, disease prediction, and personalized treatment, with the goal of improving decision-making and advancing healthcare outcomes.

  2. Enhancing Anomaly Detection in System Logs with Machine Learning (for cybersecurity)

    3. Anomaly detection plays a critical role in cybersecurity by uncovering abnormal or potentially malicious system behaviors. Modern computing systems continuously generate logs—detailed records of runtime events—that serve as a primary data source for identifying such anomalies. Traditionally, security analysts relied on manual log inspection to detect suspicious activities. However, as systems grow in scale and complexity, manual analysis becomes insufficient, inefficient, and prone to human error. My research focuses on leveraging machine learning and deep learning techniques to enhance cybersecurity by automatically detecting anomalies in system logs, enabling faster, more accurate, and scalable threat detection.

Teaching Experience

Since 2018, I have been teaching the following courses at the École Supérieure en Informatique de Sidi Bel Abbès:

  • Algorithmique et structures de données dynamiques (1ère année Classe Préparatoire)
  • Bureautique et web (1ère année Classe Préparatoire)
  • Introduction au système d’exploitation 2 (1ère année Classe Préparatoire)
  • Projet pluridisciplinaire (2ème année Classe Préparatoire, 2ème année Second Cycle de Spécialité : Intelligence Artificielle et Sciences de Données (IASD))
  • Bases de Données (1ère année Second Cycle)
  • Complexité et Résolution de Problème (2ème année Second Cycle de Spécialité : Intelligence Artificielle et Sciences de Données (IASD))

Academic and Research Engagements

  • I completed a research internship at the Department of Computer Science, Sapienza University of Rome, Italy. The internship took place from April 12, 2024, to April 22, 2024. During this period, I collaborated with Professor Enrico Tronci on combining model checking and testing techniques to enhance fault localization in imperative programs.
    You can access the report here.

  • I completed a research internship on log-based anomaly detection using machine learning for system reliability. The internship took place from July 7, 2025, to July 17, 2025. During this period, I worked under the supervision Professor Enrico Tronci. My work focused on analyzing system logs and applying both supervised and unsupervised learning techniques to detect anomalies that could impact system reliability.
    You can access the report here.

  • I completed a research internship at the ReDCAD Laboratory, Sfax University, Tunisia, from December 12, 2025, to December 22, 2025, under the supervision Dr. Ismail Bouassida. During this period, I worked on evaluating machine learning models for intrusion detection in cybersecurity, with a focus on the NSL-KDD dataset. The aim of this internship was to enhance research capacity and foster international cooperation between Arab and Muslim countries in cybersecurity research.
    You can access the report here.

Publications & Talks

 Supervised Machine Learning Approaches for Log-Based Anomaly Detection: A Case Study on the Spirit Dataset Improving Anomaly Detection in the HDFS Dataset with Novel Machine Learning Models and Techniques Log-based Anomaly Detection using BiLSTM-Autoencoder
  • Conference: The 7th International Conference on Networking and Advanced Systems (ICNAS 2025)
  • Date: October 29–30, 2025
  • Location: University Chadli Bendjedi, El Tarf, Algeria
  • Authors: Mohammed Bekkouche, Melissa Meski, Yousra Khodja, Sidi Mohammed Benslimane
  • Organized by: Laboratory LRS, University of Annaba
  • Link to the conference: ICNAS 2025 Website
  • Link to the article: Read the article
  • Presentation:
Log-Based Anomaly Detection: Enhancing System Reliability with Machine Learning Combining Model Checking and Spectrum-Based Fault Localization with Multiple Counterexamples
  • Conference: The International Symposium on iNnovative Informatics of Biskra (IEEE-ISNIB'2025)
  • Date: Jan 29-30th , 2025
  • Location: Biskra, Algeria
  • Organized by: Mohamed khider University
  • Link to the conference: IEEE-ISNIB'2025
  • Link to the article: Read the paper
  • Presentation:
Model Checking-Enhanced Spectrum-Based Fault Localization A bounded constraint-based approach to aid in fault localization from a counterexample Correcting Instruction Expression Logic Errors with GenExp: A Genetic Programming Solution Locating Loop Errors in Programs: A Scalable and Expressive Approach using LocFaults
  • Conference: Tunisian-Algerian Joint Conference on Applied Computing (TACC 2023)
  • Date: November 6 - 8, 2023
  • Location: Sousse, Tunisia
  • Link to the conference: TACC 2023
  • Link to the article: Read the paper
  • Presentation:

"Exploration of the scalability of LocFaults approach for error localization with While-loops programs." Hal-01132781
Mohammed Bekkouche.
Research report.

"Exploration de la scalabilité de LocFaults." Hal-01132780
Mohammed Bekkouche.
Research report.