Published in Journal of Computer Virology and Hacking Techniques, 2026
This paper is about our GPT-based self-supervised anomaly detection system for command lines
Recommended citation: Miles Q. Li, Julien Keutchayan, François Charest, and Benjamin C.M. Fung. GPT-based Self-supervised Anomaly Detection in Command Lines. Journal of Computer Virology and Hacking Techniques, Springer, 2026.
Published in arXiv preprint / Under review for ICML 2026, 2025
This paper presents ODCV-Bench, a safety benchmark for evaluating constraint violations in autonomous AI agents.
Recommended citation: Miles Q. Li, Benjamin Fung, Martin Weiss, Pulei Xiong, Khalil Al-Hussaeni, and Claude Fachkha. A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents. arXiv preprint arXiv:2512.20798 (2025). https://arxiv.org/abs/2512.20798
Published in Journal of Information Security and Applications, 2025
This paper is about our study on the security concerns with LLMs
Recommended citation: Miles Q. Li, and Benjamin CM Fung. Security Concerns for Large Language Models: A Survey. Journal of Information Security and Applications 95 (2025): 104284. https://www.sciencedirect.com/science/article/pii/S2214212625003217?casa_token=8Ce8QlKHMEoAAAAA:Dy_eO6f0zDbNjuXcwnPBnT9ezs0QQu8Ne_sn1DThh55aw4u-QP4OL0PbOIWzlL_ydi8uhlsP4w
Published in IJCNN 2025, 2025
This paper is about our study on training a 1.7B LLaMa model
Recommended citation: Miles Q. Li, Benjamin CM Fung, and Shih-Chia Huang. Training Dynamics of a 1.7B LLaMa Model: A Data-Efficient Approach. In Proceedings of the International Conference on Joint Conference on Neural Networks (IJCNN), pages 1-10, Roma, Italy: IEEE, June 2025. https://arxiv.org/abs/2412.13335
Published in arXiv preprint / Under review for International Journal of Data Science and Analytics, 2024
This paper investigates the effectiveness of incremental training strategies for large language models.
Recommended citation: Miles Q. Li, Benjamin Fung, and Shih-Chia Huang. On the Effectiveness of Incremental Training of Large Language Models. arXiv preprint arXiv:2411.18700 (2024). https://arxiv.org/abs/2411.18700
Published in Journal of Expert Systems with Applications, 2024
This paper is about our survey paper on explainable AI
Recommended citation: Abusitta, Adel, Miles Q. Li, and Benjamin CM Fung. "Survey on Explainable AI: Techniques, challenges and open issues." Expert Systems with Applications (2024): 124710. https://www.sciencedirect.com/science/article/pii/S095741742401577X
Published in Procedia Computer Science, 2023
This paper proposes a hybrid neural network combining memory networks and multi-head attention for detecting vulnerable lines of code.
Recommended citation: Miles Q. Li, Benjamin CM Fung, and Ashita Diwan. "A Novel Deep Multi-head Attentive Vulnerable Line Detector." Procedia Computer Science 222 (2023): 35-44. https://www.sciencedirect.com/science/article/pii/S1877050923009079
Published in ICMLA 2022, 2022
This paper proposes an automated deep learning method to generate representations of assembly code for vulnerability detection.
Recommended citation: Ashita Diwan, Miles Q. Li, and Benjamin CM Fung. "VDGraph2Vec: Vulnerability Detection in Assembly Code Using Message Passing Neural Networks." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1039-1046. IEEE, 2022. https://ieeexplore.ieee.org/document/10069134/
Published in Journal of Computers & Security, 2022
This paper is about our novel malware detection model
Recommended citation: Miles Q. Li, Benjamin CM Fung, Philippe Charland, and Steven HH Ding. "I-MAD: Interpretable malware detector using Galaxy Transformer." Computers & Security 108 (2021): 102371. https://www.sciencedirect.com/science/article/pii/S0167404821001954
Published in IJCNN 2022, 2022
This paper is about our novel interpretable feedforward neural network
Recommended citation: Li, M. Q., Fung, B. C. M., & Abusitta, A. On the Effectiveness of Interpretable Feedforward Neural Network. In Proceedings of the International Conference on Joint Conference on Neural Networks (IJCNN), pages 1-8, Padova, Italy: IEEE, July 2022. https://arxiv.org/pdf/2111.02303.pdf
Published in ICSOFT 2022, 2022
This paper proposes a novel interpretable malware classification model based on functional analysis.
Recommended citation: Miles Q. Li and Benjamin CM Fung. "Interpretable Malware Classification based on Functional Analysis." In ICSOFT, pp. 500-507. 2022. https://www.scitepress.org/Papers/2022/113109/113109.pdf
Published in Journal of Information Sciences, 2022
This paper is about our novel black-box adversarial defense method
Recommended citation: Li, Miles Q., Benjamin CM Fung, and Philippe Charland. "DyAdvDefender: An instance-based online machine learning model for perturbation-trial-based black-box adversarial defense." Information Sciences (2022). https://www.sciencedirect.com/science/article/pii/S0020025522003747?casa_token=p5N50hWOf0oAAAAA:OoG3up9I8-W8kW1zutzK3zuzOZL1kpWspm_7h0YJZC_aowNcFvN97aUNwcWJvMX61QngMi4aNjy4
Published in Journal of Information Security and Applications, 2022
This paper is about our survey paper on malware detection
Recommended citation: Abusitta, Adel, Miles Q. Li, and Benjamin CM Fung. "Malware classification and composition analysis: A survey of recent developments." Journal of Information Security and Applications 59 (2021): 102828. https://www.sciencedirect.com/science/article/pii/S2214212621000648
Published in International Conference on Software Technologies (Springer), 2021
This paper proposes a neural network-based malware severity classification method.
Recommended citation: Miles Q. Li and Benjamin CM Fung. "A Novel Neural Network-Based Malware Severity Classification System." In International Conference on Software Technologies, pp. 218-232. Springer, 2021. https://link.springer.com/chapter/10.1007/978-3-031-11513-4_10
Published in ICSOFT 2021, 2021
This paper proposes a novel dedicated machine learning model for malware classification.
Recommended citation: Miles Q. Li, Benjamin CM Fung, Philippe Charland, and Steven HH Ding. "A Novel and Dedicated Machine Learning Model for Malware Classification." In ICSOFT, pp. 617-628. 2021. https://dmas.lab.mcgill.ca/fung/pub/LFCD21icsoft_postprint.pdf
Published in EMNLP 2016, 2016
This paper is about our discourse parsing model
Recommended citation: Qi Li, Tianshi Li, and Baobao Chang. "Discourse Parsing with Attention-based Hierarchical Neural Networks." EMNLP. 2016. https://aclanthology.org/D16-1035.pdf