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Li Yang
PhD

Assistant Professor

Information Technology

Faculty of Business and Information Technology

LinkedIn

Contact information

Business and Information Technology Building - Room 3006
North Oshawa
2000 Simcoe Street North
Oshawa, ON L1G 0C5

905.721.8668 ext. 3806

li.yang@ontariotechu.ca
Li Yang Website

Research topics

  • Cybersecurity
  • machine learning, deep learning
  • AutoML
  • model optimization
  • network data analytics
  • Internet of Things (IoT)
  • 5G/6G networks
  • intrusion detection
  • anomaly detection
  • concept drift
  • online learning
  • continual learning
  • adversarial machine learning

Areas of expertise

  • Information Technology
  • Artificial Intelligence
  • Big Data

Background

Li Yang is an Assistant Professor in the Faculty of Business and Information Technology at Ontario Tech University. He received his Ph.D. in Electrical and Computer Engineering from Western University in 2022. He was the vice chair of the IEEE Computer Society, London Section, Canada, from 2022 to 2023. He was also on the technical program committee for IEEE GlobeCom 2023, the workshop chair for SMC-IoT 2023, and the technical session chair for IEEE CCECE 2020. His papers and code publications have received thousands of citations and GitHub stars. His research interests include cybersecurity, machine learning, deep learning, AutoML, model optimization, network data analytics, network automation for 6G, Internet of Things (IoT), intrusion detection, anomaly detection, concept drift, online learning, continual learning, and adversarial machine learning. 

Education

  • PhD Western University 2022
  • Master of Science University of Guelph 2018
  • Bachelor of Engineering Wuhan University of Science and Technology 2016

Courses taught

Machine Learning

Research and expertise

My research focuses on applying AI and machine learning to cybersecurity, with a particular emphasis on intrusion detection and anomaly detection in 5G/6G networks and IoT systems. This involves the development of optimized and Automated ML (AutoML), concept drift adaptation, online learning, and continual learning techniques to enhance cybersecurity measures. Additionally, my work extends to the realms of trustworthy AI and AI security, specifically addressing adversarial machine learning attacks and defense strategies.

Research Grants and Awards:

  • Graduate Student Award for Excellence in Research, London, Canada, 2022 
  • Graduate Symposium Award for Best Presentation, London, Canada, 2022 
  • Chinese Government Award for Outstanding Self Finance Students Abroad, China, 2022
  • Mitacs Accelerate Fellowship, 2021
  • The OC2 Lab Industrial Research Excellence Award, London, Canada, 2020
  • The OC2 Lab Research Excellence Award, London, Canada, 2020

 

Involvement

  • Journal Papers & Book Chapters

    L. Yang and A. Shami, "A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems," IEEE Transactions on Industrial Informatics (Impact Factor: 11.648), vol. 19, no. 2, pp. 2107-2116, 2023.

    L. Yang, A. Moubayed, A. Shami, A. Boukhtouta, P. Heidari, S. Preda, R. Brunner, D. Migault, and A. Larabi, “Forensic Data Analytics for Anomaly Detection in Evolving Networks”, A book chapter in Innovations in Digital Forensics, World Scientific, 2023.

    L. Yang and A. Shami, “IDS-ML: An open source code for Intrusion Detection System development using Machine Learning,” Software Impacts (CiteScore: 1), Elsevier, vol. 14, p. 100446, 2022.

    L. Yang and A. Shami, “IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective,” Engineering Applications of Artificial Intelligence (Impact Factor: 7.802), Elsevier, vol. 116, pp. 1–33, 2022.

    L. Yang, A. Moubayed, A. Shami, P. Heidari, A. Boukhtouta, A. Larabi, R. Brunner, S. Preda, and D. Migault, “Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection,” IEEE Transactions on Network and Service Management (Impact Factor: 4.758), vol. 19, no. 1, pp. 686-705, Mar. 2022.

    L. Yang, A. Moubayed and A. Shami, “MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles,” IEEE Internet of Things Journal (Impact Factor: 10.238), vol. 9, no. 1, pp. 616-632, Jan. 2022.

    L. Yang and A. Shami, “A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams,” IEEE Internet of Things Magazine (SJR: 2.607), vol. 4, no. 2, pp. 96-101, Jun. 2021.

    L. Yang and A. Shami, “On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice,” Neurocomputing (Impact Factor: 5.779), Elsevier, vol. 415, pp. 295–316, 2020.

    L. Yang, R. Muresan, A. Al-Dweik and L. Hadjileontiadis, "Image-Based Visibility Estimation Algorithm for Intelligent Transportation Systems," IEEE Access (Impact Factor: 3.476), vol. 6, pp. 76728-76740, 2018.

  • Conference Papers

    L. Yang, A. Shami, G. Stevens, and S. DeRusett, “LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles,” in 2022 IEEE Global Communications Conference (GLOBECOM), 2022, pp. 1-6.

    L. Yang and A. Shami, “A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles,” in 2022 IEEE International Conference on Communications (ICC), 2022, pp. 1-6.

    L. Yang, D. M. Manias, and A. Shami, “PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams,” in 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-6.

    D. M. Manias, I. Shaer, L. Yang, and A. Shami, "Concept Drift Detection in Federated Networked Systems, " in 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-6.

    L. Yang, A. Moubayed, I. Hamieh, and A. Shami, “Tree-based Intelligent Intrusion Detection System in Internet of Vehicles,” in 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1-6.

  • Thesis

    L. Yang, "Optimized and Automated Machine Learning Techniques Towards IoT Data Analytics and Cybersecurity", Ph.D. Thesis, Western University, 2022.

    L. Yang, "Comprehensive visibility indicator algorithm for adaptable speed limit control in intelligent transportation systems", M.A.Sc. Thesis, University of Guelph, 2018. 

  • Patents
    L. Yang, "Convenient primary-secondary barrels", CN201268426Y, 2009.

Affiliations

Western University, London, Ontario, Canada

University of Guelph, Guelph, Ontario, Canada