Welcome!

I am a 2nd-year PhD researcher pursuing a joint doctoral program between Lebanese University and CESI École d'Ingénieurs, France. Currently based at CESI LINEACT laboratory in Strasbourg, I focus on developing novel intrusion detection mechanisms for cyber-physical systems using federated Graph Neural Networks.

Recent Achievement:

Received "Very Encouraging Results" rating in September 2025 Individual Follow-up Committee (CSI) evaluation

Current Research Focus

Intrusion Detection for Cyber-Physical Systems

My doctoral research centers on developing advanced machine learning approaches for cybersecurity, specifically targeting:

  • Federated Graph Neural Networks for distributed IDS
  • Privacy-preserving intrusion detection
  • Community-based learning approaches
  • IoT and cyber-physical systems security
  • Graph-based anomaly detection
  • Scalable cybersecurity datasets
  • Real-time threat identification
  • Distributed learning architectures

Publications & Research Output

6

Published Papers

3

Under Review

Published Journal Papers

Graph-based Federated Learning Approach for Intrusion Detection in IoT Networks

Published

Scientific Reports (Nature Portfolio) • Q1 Journal

Privacy-preserving federated learning approach combining Graph Attention Networks with community-based abstraction for IoT network security.

DOI: 10.1038/s41598-025-25175-1

MOTION: Multi-Models Correlation Framework for Energy-Saving in Wireless Video Sensor Networks

Published

Computers and Electrical Engineering (Elsevier) • Q1 Journal

Advanced correlation framework for wireless video sensor networks optimization.

DOI: 10.1016/j.compeleceng.2025.110720

Published Conference Papers

SPARKLE: Structured Parsing for Arabic Resource Knowledge and Language Extraction

Published

AINA 2025: The 39th International Conference on Advanced Information Networking and Applications

Advanced NLP framework for Arabic text processing with cross-media content integration.

View on GitHub DOI: 10.1007/978-3-031-87766-7_25

Generating Realistic Cyber Security Datasets for IoT Networks with Diverse Complex Network Properties

Published

IoTBDS 2025: 10th International Conference on Internet of Things, Big Data and Security

Novel approach to generating diverse IoT cybersecurity datasets with complex network properties.

View on GitHub DOI: 10.5220/0013359000003944

SAGE: Semantic Adaptation of Graph Embeddings for Arabic Entity Linking

Accepted

AICCSA 2025: IEEE Conference on Computer Science and its Applications, Doha, Qatar

Semantic graph embeddings for Arabic natural language processing.

SHOLO: Similarity-based Reduction Scheme for Efficient Data Transmission and Energy Saving in Multimedia Sensor Networks

Accepted

WiMob 2025: 21st International Conference on Wireless and Mobile Computing, Networking and Communications

Energy-saving framework for multimedia sensor networks using similarity detection.

Under Review

Graph-Temporal Learning for IoT Intrusion Detection in Centralized and Federated Settings via Adaptive Fusion Gating

Under Review

Journal of Network and Systems Management (Springer Nature)

Adaptive fusion framework combining graph and temporal features for intrusion detection.

Community-based Vulnerability Prediction Framework for IoT Intrusion Detection using only Network Topology

Under Review

Future Generation Computer Systems (Elsevier)

Topology-based vulnerability prediction using structural network measures.

SHAMIL: A Comprehensive Framework for Arabic Natural Language Processing with Dynamic Knowledge Graph Integration

Under Review

Array (Elsevier)

Dynamic knowledge graph integration for Arabic natural language processing.

Academic Background

Joint PhD in Computer Science (2024-Present)

Lebanese University & CESI École d'Ingénieurs, France

Currently conducting research at CESI LINEACT UR 7527 laboratory in Strasbourg under the supervision of leading experts in cybersecurity and machine learning.

Master's in Data Science for Risk Analysis (2022-2024)

Lebanese University • Ranked 1st in class

Specialized in risk analysis and data science with thesis on ontology-based Arabic NLP systems. Led the development of the SHAMIL framework demonstrating technical leadership and innovation.

Technical Expertise

Machine Learning & AI

  • Graph Neural Networks
  • Federated Learning
  • Deep Learning
  • TensorFlow/PyTorch

Programming

  • Python, Java, C++
  • JavaScript, PHP
  • Django, ASP.NET
  • Git, Docker

Data & Security

  • Neo4j, MongoDB
  • Cybersecurity
  • Network Analysis
  • Big Data Analytics

Personal Interests

⚽ Football Playing & Watching
🏊‍♂️ Swimming Fitness & Recreation
🥾 Hiking Exploring Nature
☕ Coffee Fuel for Research