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Ahmed Bensaoud

Ahmed Bensaoud headshot

Assistant Professor

Computing Sciences

College of Engineering, Technology, and Architecture

Ph.D., Computer Science, University of Colorado Colorado Springs, 2023 (Concentration: Artificial Intelligence and Cybersecurity)

Dr. Ahmed Bensaoud is an Assistant Professor of Computing Sciences at the University of Hartford. He earned his Ph.D. in Computer Science from the University of Colorado, Colorado Springs in 2023, specializing in Artificial Intelligence and Cybersecurity. His research interests include adaptive AI systems, advanced malware detection, vulnerability management in Large Language Models (LLMs), computer vision, and natural language processing.

At the University of Hartford, Dr. Bensaoud teaches courses in Intrusion Detection and Security, Artificial Intelligence, Penetration Testing and Vulnerability, Deep Learning Applications, Internet Programming, Software Development, and Computer Networking. His teaching philosophy focuses on hands-on learning, critical thinking, and collaboration, with a strong commitment to mentoring students both in and outside the classroom. Beyond academia, he has submitted AI research proposals to the National Science Foundation (NSF) and enjoys exploring emerging technologies, traveling to scenic destinations, and volunteering in educational programs that serve the community.

  • Outstanding Graduate Student of the Year - University of Colorado Colorado Springs, May 2023
  • Colorado Cybersecurity Scholarship - Aug 2022,
  • Colorado Cybersecurity Scholarship - Aug 2021,
  • Graduate Research Fellowship - Aug 2020,
  • Graduate School Tuition Matching Grant – Jan 2020,
  • Graduate Assistant Research - Aug 2019.
  • Artificial Intelligence & Machine Learning
  • Cybersecurity & Intrusion Detection
  • Malware Analysis & Threat Detection
  • Large Language Models (LLMs) Security & Vulnerability Management
  • Computer Vision & Natural Language Processing
  • Deep Learning Applications in Cyber Defense
  • A novel active learning approach to label one million unknown malware variants
    A Bensaoud, J Kalita
    International Journal of Approximate Reasoning, 182, 109426, 2025
  • CleanSheet: Advancing backdoor attack techniques for deep neural networks with stealthy trigger embedding
    A Bensaoud, J Kalita
    Systems and Soft Computing, 200335, 2025
  • Advancing software security: DCodeBERT for automatic vulnerability detection and repair
    A Bensaoud, J Kalita
    Journal of Industrial Information Integration, 45, 100834, 2025
  • Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models
    A Bensaoud, J Kalita
    Ad Hoc Networks, 170, 103770, 2025
  • A survey of malware detection using deep learning
    A Bensaoud, J Kalita, M Bensaoud
    Machine Learning With Applications, 16, 100546, 2024
  • CNN-LSTM and transfer learning models for malware classification based on opcodes and API calls
    A Bensaoud, J Kalita
    Knowledge-Based Systems, 290, 111543, 2024
  • Deep multi-task learning for malware image classification
    A Bensaoud, J Kalita
    Journal of Information Security and Applications, 64, 103057, 2022
  • Classifying malware images with convolutional neural network models
    A Bensaoud, N Abudawaood, J Kalita
    arXiv preprint arXiv:2010.16108, 2020