Benamar BOUYEDDOU

Lecturer-researcher in Telecommunications

Department of Telecommunications Faculty of Mathematics and Computer Science

About

Dr Benamar Bouyeddou is a lecturer-researcher in the telecommunications department of the University of Saida Dr Moulay Tahar and a member of the LESM laboratory. His research focuses on anomaly detection, network security and cyber-attack detection in industrial systems and smart grids, using deep learning methods and statistical approaches.

Research areas

Anomaly detectionNetwork securityCyber-attack detectionIndustrial systems and smart-grid securityDeep learning

Scientific publications

20241 paper

Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
Future Internet, 16(6): 184

20231 paper

Semi-Supervised Deep Learning-Driven Anomaly Detection Schemes for Cyber-Attack Detection in Smart Grids
Power Systems Cybersecurity (Springer)

20222 papers

Cyber-Attacks Detection in Industrial Systems Using Artificial Intelligence-Driven Methods
International Journal of Critical Infrastructure Protection, 38: 100542
DOI: 10.1016/j.ijcip.2022.100542
A Stacked Deep Learning Approach to Cyber-Attacks Detection in Industrial Systems
Cluster Computing, 25(1): 561-578

20211 paper

Detecting Network Cyber-Attacks Using an Integrated Statistical Approach

Same department

Department colleagues

The department →

Boubakar Seddik BOUAZZA

Full Professor of Telecommunications

Abdelhamid CHERIFI

Professor of Telecommunications

Keltouma NOURI

Professor of Telecommunications

About

Research areas

Anomaly detectionNetwork securityCyber-attack detectionIndustrial systems and smart-grid securityDeep learning

Scientific publications

20241 paper

Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
Future Internet, 16(6): 184

20231 paper

Semi-Supervised Deep Learning-Driven Anomaly Detection Schemes for Cyber-Attack Detection in Smart Grids
Power Systems Cybersecurity (Springer)

20222 papers

Cyber-Attacks Detection in Industrial Systems Using Artificial Intelligence-Driven Methods
International Journal of Critical Infrastructure Protection, 38: 100542
DOI: 10.1016/j.ijcip.2022.100542
A Stacked Deep Learning Approach to Cyber-Attacks Detection in Industrial Systems
Cluster Computing, 25(1): 561-578

20211 paper

Detecting Network Cyber-Attacks Using an Integrated Statistical Approach

Benamar BOUYEDDOU

About

Research areas

Scientific publications

Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids

Semi-Supervised Deep Learning-Driven Anomaly Detection Schemes for Cyber-Attack Detection in Smart Grids

Cyber-Attacks Detection in Industrial Systems Using Artificial Intelligence-Driven Methods

A Stacked Deep Learning Approach to Cyber-Attacks Detection in Industrial Systems

Detecting Network Cyber-Attacks Using an Integrated Statistical Approach

Department colleagues

Boubakar Seddik BOUAZZA

Abdelhamid CHERIFI

Keltouma NOURI

Benamar BOUYEDDOU

About

Research areas

Scientific publications

Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids

Semi-Supervised Deep Learning-Driven Anomaly Detection Schemes for Cyber-Attack Detection in Smart Grids

Cyber-Attacks Detection in Industrial Systems Using Artificial Intelligence-Driven Methods

A Stacked Deep Learning Approach to Cyber-Attacks Detection in Industrial Systems

Detecting Network Cyber-Attacks Using an Integrated Statistical Approach

Department colleagues

Boubakar Seddik BOUAZZA

Abdelhamid CHERIFI

Keltouma NOURI

An Improved Multivariate Chart Using Partial Least Squares With Continuous Ranked Probability Score