Wafa Mefteh

dblp:03/11202 · DBLP profile ↗
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13ranked-venue papers
3as first author
11since 2021 · last 2026
0000-0002-3543-885XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 1 first-author · 5 since 2021Applied, interdisciplinary, general and emerging computing · 6 · 2 first-author · 5 since 2021Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Cooperative Agents Based Federated Learning for Ransomware Detection and Response in Cloud Systems
Abderrahmen Chaieb, Wafa Mefteh, Ali Frihida
ICAART (5)2
2026 Cooperative Intelligent Agents Based Approach for Proactive Defense and Geolocation of BGP Hijacking Attacks in SD-WAN Systems
Hichem Dachraoui, Wafa Mefteh, Ali Frihida
ICAART (3)2
2026 Intelligent Multi-agent Based Detection and Response to Data Exfiltration in Distributed Cloud
Kamel Bouallègui, Wafa Mefteh, Mohamed Koubàa
WorldCIST (4)2
2026 Intelligent Cooperative Agents for DDoS Attack Detection and Response in Distributed Cloud
Walid Khazri, Wafa Mefteh, Mohamed Koubàa
WorldCIST (4)2
2026 The Power of GeoSpatial Data Mining and Geo-AI: A Comprehensive Review and Concise Guide for New Developers
Wafa Mefteh, Ali Frihida
WorldCIST (3)1
2026 Intrusion detection system-based ensemble machine learning to improve IoT network against cyber attacks
Shayma Wail Nourildean, Wafa Mefteh, Ali Frihida
J. Supercomput.2
2025 Hybrid Deep Learning Model-Based Intrusion Detection System to Improve Artificial Internet of Things Against Cyber Attacks
Shayma Wail Nourildean, Wafa Mefteh, Ali Frihida
AINA (8)2
2024 Simulation Based Design of Self-Adaptive Agent Behavior: Road Traffic Data Collection Case Study
abstract
Data collection is presented as the fundamental process of a computer project, an industrial project, or a research study. It gathers high-quality data that is then used for precise and intricate analysis. Therefore, through the process of data collection, one can formulate answers to relevant questions, enhance decision-making, and evaluate results. On the other hand, due to the vast amount of data, this process becomes increasingly complicated, necessitating the use of advanced tools and modern methods. In this context, we introduce a new data collection process based on agent software. Specifically, our focus is on self-adaptive agent. In our research, we are interested in collecting data in the field of transportation. Thus, this paper presents a case study illustrating the simulation-based design of a road traffic data collection self-adaptive agent.
Karima Gouasmia, Wafa Mefteh, Faïez Gargouri
IS2
2024 Review on deep learning classifiers for faults diagnosis of rotating industrial machinery
Ameer Ali Shaalan, Wafa Mefteh, Ali Frihida
Serv. Oriented Comput. Appl.2
2022 Machine Learning for Complex Data Analysis: Overview and a Discussion of a New Reinforcement-Learning Strategy
Karima Gouasmia, Wafa Mefteh, Faïez Gargouri
ISDA (2)2
2022 Mobile and Cooperative Agent Based Approach for Intelligent Integration of Complex Data
Karima Gouasmia, Wafa Mefteh, Faïez Gargouri
ISDA (4)2
2020 Complex Systems Modeling Overview About Techniques and Models and the Evolution of Artificial Intelligence
Wafa Mefteh, Mohamed-Anis Mejri
WorldCIST (1)1
2015 ADELFE 3.0 Design, Building Adaptive Multi Agent Systems Based on Simulation a Case Study
Wafa Mefteh, Frédéric Migeon, Marie-Pierre Gleizes, Faïez Gargouri
ICCCI (1)1