← Research portfolio

Fahdah Alalyan

PhD candidate in Computer Engineering · École de technologie supérieure · Montréal

Expected completion: August 2027

Fahdah.a15@gmail.com · LinkedIn · IEEE DataPort

Research interests

I research machine learning for secure mobile networks, with a focus on federated learning, quantum machine learning, and cyberattack detection in 5G and Open RAN. My work spans experimental network testbeds and statistical methods for classification and clustering.

Education

PhD in Computer Engineering

École de technologie supérieure (ÉTS), Université du Québec

September 2023–present

MASc in Information Systems Security

Concordia University

2020

BSc in Information Systems

Princess Nourah bint Abdulrahman University

2013

Research and technical experience

Research Assistant

École de technologie supérieure · January 2023–present

Developing AI-based algorithms for cyberattack detection in 5G and O-RAN, with a focus on federated learning and quantum machine learning.

Summer Research Assistant

Carleton University · June–August 2026

Conducted experimental research on RF jamming resilience in O-RAN-based terrestrial and non-terrestrial networks.

CyberPro+ Trainee

National Cybersecurity Authority and SITE · July 2021–January 2022

Training in operating systems, networking, cyber defence, penetration testing, and threat detection.

Data Scientist Intern

General Assembly · December 2020–February 2021

Built and evaluated machine-learning models for data-science problems.

Research Assistant in ML and Data Mining

Concordia University · January 2018–October 2020

Developed methods for binary-data clustering, mixture-model hierarchical clustering, and probabilistic kernels.

Publications

  1. Badre Bousalem, Fahdah Alalyan, Vinicius F. Silva, Sylvain Cherrier, Wael Jaafar, Rami Langar. SAFE-ADVENT: Privacy-Preserving and Resilient Distributed Federated Learning for DDoS Attacks Detection in 5G-V2X Networks. IEEE Transactions on Machine Learning in Communications and Networking, 2026.
  2. Fahdah Alalyan, Jihene Saidi, Wael Jaafar, Rami Langar. Quantum Machine Learning for Anomaly Detection in 5G and Beyond Mobile Networks. Global Information Infrastructure and Networking Symposium (GIIS), 2026.
  3. Fahdah Alalyan, Mirna Awad, Wael Jaafar, Rami Langar. Secure Distributed Federated Learning for Cyberattacks Detection in B5G Open Radio Access Networks. IEEE Open Journal of the Communications Society, 2025.
  4. Fahdah Alalyan, Badre Bousalem, Wael Jaafar, Rami Langar. Secure Peer-to-Peer Federated Learning for Efficient Cyberattacks Detection in 5G and Beyond Networks. IEEE International Conference on Communications (ICC), 2024.
  5. Fahdah Alalyan, Nuha Zamzami, Nizar Bouguila. A Hybrid Approach Based on SVM and Bernoulli Mixture Model for Binary Vectors Classification. IEEE International Conference on Systems, Man and Cybernetics (SMC), 2020.
  6. Fahdah Alalyan, Nuha Zamzami, Manar Amayri, Nizar Bouguila. An Improved K-medoids Algorithm Based on Binary Sequences Similarity Measures. International Conference on Control, Decision and Information Technologies (CoDIT), 2019.
  7. Fahdah Alalyan, Nuha Zamzami, Nizar Bouguila. Model-Based Hierarchical Clustering for Categorical Data. IEEE International Symposium on Industrial Electronics (ISIE), 2019.

Manuscript under review

Quantum GAN-Assisted Peer-to-Peer Federated Learning for Decentralized O-RAN Cyberattacks Detection. Submitted to IEEE Transactions on Machine Learning in Communications and Networking. Under review; not yet an accepted publication.

Technical skills

Python, TensorFlow, Keras, Scikit-learn, PyTorch, MATLAB, C++, SQL, Git, Linux, Virtual machines

Machine learning, Quantum machine learning, Federated learning, Deep learning, Transfer learning, Anomaly detection, Data preprocessing, Classification, Clustering, Data analysis and modeling

Awards

Languages

Arabic · English · French (beginner)

Updated October 2026. Use your browser’s print option to save a PDF copy.