Dr. Farah Jemili

Address:

University Of Sousse, Sousse, Tunisia
 

Academic Metrics

• H-Index: 15
• I10-Index: 23
• Total Citations: 735

Research Interests:

• Artificial Intelligence And Deep Learning
• Cyber Security And Intrusion Detection Systems (IDS)
• Big Data Analysis And Distributed Systems
• Uncertainty Analysis And Bayesian Inference
• IoT Security And Blockchain Technology

Biography:

Dr. Farah Jemili is a seasoned Associate Professor and researcher specializing in the convergence of Artificial Intelligence (AI) and Cyber Security. Her primary research focuses on developing sophisticated Intrusion Detection Systems (IDS) using techniques like Deep Learning, Bayesian Inference, and Big Data Analytics. Dr. Jemili has contributed to international research programs like Horizon Europe and previously served as the Head of the Computer Science Department at ISITCom. Her work directly contributes to enhancing network security, distributed systems, and modern IoT environments.
 

Education Details:

• PhD: Computer Sciences (National School Of Computer Sciences (ENSI), University Of Mannouba, Tunisia, 2010)
• Master's Degree: Computer Sciences (ENSI, University Of Mannouba, Tunisia, 2004)
• Engineer Degree: Computer Science (ENSI, Tunisia, 2002)
   

Professional Background:

• Associate Professor (Current, Since 2007) - Higher Institute Of Computer Science And Telecom (ISITCom), University Of Sousse.
• Senior Researcher (Current) - MARS Laboratory (Modeling Of Automated Reasoning Systems), University Of Sousse.
• Head Of The Department Of Computer Science (2017–2020) - ISITCom Hammam Sousse.
• Member: PMO (Project Management Office), University Of Sousse (Since 2019).
   

Current Research Projects:

• Intelligent Control Agents: Developing machine learning-enhanced intelligent control agents for IoT network attack detection.
• Intrusion Detection Systems (IDS): Research into using Transformer Models and Ensemble Learning for detecting unknown intrusions in large heterogeneous data.
• Blockchain Security: Investigating the use of Blockchain technology with deep learning for robust intrusion detection in Cyber-Physical Systems.
   

Academic Profiles Of Dr. Farah Jemili

Explore his academic and professional presence across trusted platforms:

• Google Scholar
• ResearchGate

Publications:

Dr. Jemili has an active and highly relevant publication record, demonstrating consistent application of advanced AI and data analytics techniques to critical network security and intrusion detection challenges.

The list of recent publications of Dr. Farah Jemili is listed below:

• Abu Munshar, H. H., Jemili, F., Korbaa, O., & Alauthman, M. (2025). Intelligent Control Agent For IoT Network Attack Detection: A Machine Learning Approach. IT Professional, 27(3), 1–8. DOI: 10.1109/MITP.2025.3556242.
• Selem, M., Jemili, F., & Korbaa, O. (2025). Deep learning for intrusion detection in IoT networks. Peer-To-Peer Networking And Applications, 28(2), 1-15. DOI: 10.1007/s12083-024-01819-3.
• Jemili, F., Jouini, K., & Korbaa, O. (2025). Detecting unknown intrusions from large heterogeneous data through ensemble learning. Intelligent Systems With Applications, 4, 200465. DOI: 10.1016/j.iswa.2024.200465.
• Jemili, F., Meddeb, R., & Korbaa, O. (2024). Intrusion detection based on ensemble learning for big data classification. Cluster Computing, 27(3), 3771-3798. DOI: 10.1007/s10586-023-04168-7.
• Aljabri, A., Jemili, F., & Korbaa, O. (2024). Convolutional neural network for intrusion detection using blockchain technology. International Journal Of Computers And Applications, 46(2), 67-77. DOI: 10.1080/1206212X.2023.2284443.

 

Last Updated on September 29, 2025