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Farah Jemili | Cybersécurité

Assist Prof Dr Farah Jemili: Leading Researcher in Cybersécurité

Assist Prof Dr Farah Jemili at University of Sousse Tunisia.

🎉🏆Congratulations, Assist Prof Dr Farah Jemili, on winning the esteemed Women Researcher Award from Young scientist Awards! Your dedication, innovative research, and scholarly contributions have truly made a significant impact in your field. Your commitment to advancing knowledge and pushing the boundaries of research is commendable. Here’s to your continued success in shaping the future of academia and making invaluable contributions to your field. Well done 🌟

👨‍🏫  Farah Jemili, an Professor at the  University of Sousse, Tunisia, stands as a distinguished academic and researcher in the Computer Science. Holding a Ph.D Assistant Professor research in artificial intelligence &Big Data Analysis at MARS lab.,IsITCom, University of Sousse, Tunisia . their professional journey exemplifies dedication and expertise. 📚

Professional Profile

Academic Qualifications

Ph.D, Eng., Assistant Professor, Researcher in Artificial Intelligence & Big Data Analysis at MARS Lab , ISITCom, University of Sousse, Tunisia

💼Employment :

Assistant Professor(FSB BIZERTE) 2004 to 2010|Employment

Farah Jemili’s citation metrics and indices from Google Scholar are as follows:

📊 Citation Metrics (Google Scholar):

Publications

32 Documents

📚Top Noted Publications (Journals)

A framework for an adaptive intrusion detection system using Bayesian network  Published in 2007/5/23 Cited by 115

Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning Published online: 25 Jul 2023.

A framework for an adaptive intrusion detection system using Bayesian network

Using MongoDB databases for training and combining intrusion detection datasets Published in 2018 Cited by 11.

A survey of attacks in mobile ad hoc networks

Anomaly-based behavioral detection in mobile Ad-Hoc networks

Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques

Distributed Architecture of an Intrusion Detection System in Industrial Control Systems Published in 2022/9/21 Cited by 8

Neuro-fuzzy and genetic-fuzzy based approaches in intrusion detection: Comparative study Published in 2017 cited by 5

OuajdiKorbaa.” Neuro-fuzzy and genetic-fuzzy based approaches in intrusion detection: Comparative study.” Software, Telecommunications and Computer Networks (SoftCOM) Published in 2017 Cited by 5

 

Farah Jemili | Cybersécurité
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