Publications

Security Concerns for Large Language Models: A Survey

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

Training Dynamics of a 1.7B LLaMa Model: A Data-Efficient Approach

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

Survey on Explainable AI: Techniques, challenges and open issues

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

I-MAD: Interpretable malware detector using Galaxy Transformer

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

On the Effectiveness of Interpretable Feedforward Neural Network

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

DyAdvDefender: An instance-based online machine learning model for perturbation-trial-based black-box adversarial defense

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

Malware classification and composition analysis: A survey of recent developments

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

Discourse Parsing with Attention-based Hierarchical Neural Networks

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

Miles Q. Li (李琦)

Publications