Saleh Sami S. Alabdulwahab

AI and Cybersecurity researcher and academic with expertise in side-channel attacks, machine learning, and privacy-preserving techniques. Proven record of developing advanced methodologies and deep learning models to enhance IoT security and intrusion detection systems. Published extensively in respected journals and demonstrated leadership in collaborative research projects, mentoring, and teaching junior researchers.

• Toastmasters International: Education Officer – Mentored public speaking and leadership development.
• Seoul International Book Fair: Represented Saudi Arabia as a technology exhibitor.

• Advanced Side-Channel Evaluation Using Contextual Deep Learning-Based Leakage Modeling
  ACM Transactions on Software Engineering and Methodology, 2025. Q1 | IF: 6.6
• Enhancing Deep Learning-Based Side-Channel Analysis Using Feature Engineering in a Fully Simulated IoT System
  Expert Systems with Applications, Q1 | IF: 7.5
• Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN
  Sensors, Q2 | IF: 3.4
• Generating Synthetic Dataset for ML-Based IDS Using CTGAN and Feature Selection to Protect Smart IoT Environments
  Applied Sciences, Vol. 13, No. 19, 2023, Article 10951. Q2 | IF: 2.5
• Feature Selection Methods Simultaneously Improve the Detection Accuracy and Model Building Time of Machine Learning Classifiers
  Symmetry, Q2 | IF: 2.2
• Pseudo-Labeling for Deep Learning-Based Side-Channel Disassembly Using Contextual Layer and Feature Engineering
  Doctoral Dissertation, Dongguk University, 2025.
• Feature Selection to Improve the Accuracy and Efficiency of Intrusion Detection Systems
  Master’s Thesis, Dongguk University, 2021.
• Design an integrated AI platform using containerized infrastructure
  Korea Smart Policing Society conference, 2024.
• Skeleton-based real-time self-harm and suicidal behavior detection
  Korea Smart Policing Society conference 2024.