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):
- Cited by: All – 398, Since 2018 – 286
- h-index: All – 11, Since 2018 – 9
- i10-index : All -11 Since 2018 -9
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