Miles Q. Li

According to our database, Miles Q. Li authored at least 11 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Survey on Explainable AI: Techniques, challenges and open issues.

Adel Abusitta, Miles Q. Li, Benjamin C. M. Fung

Expert Syst. Appl., 2024

2023

A Novel Deep Multi-head Attentive Vulnerable Line Detector.

Miles Q. Li, Benjamin C. M. Fung, Ashita Diwan

Proceedings of the International Neural Network Society Workshop on Deep Learning Innovations and Applications, 2023

2022

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

Miles Q. Li, Benjamin C. M. Fung, Philippe Charland

Inf. Sci., 2022

On the Effectiveness of Interpretable Feedforward Neural Network.

Miles Q. Li, Benjamin C. M. Fung, Adel Abusitta

Proceedings of the International Joint Conference on Neural Networks, 2022

Interpretable Malware Classification based on Functional Analysis.

Miles Q. Li, Benjamin C. M. Fung

Proceedings of the 17th International Conference on Software Technologies, 2022

VDGraph2Vec: Vulnerability Detection in Assembly Code using Message Passing Neural Networks.

Ashita Diwan, Miles Q. Li, Benjamin C. M. Fung

Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

2021

Malware classification and composition analysis: A survey of recent developments.

Adel Abusitta, Miles Q. Li, Benjamin C. M. Fung

J. Inf. Secur. Appl., 2021

<i>I-MAD</i>: Interpretable malware detector using Galaxy Transformer.

Miles Q. Li, Benjamin C. M. Fung, Philippe Charland, Steven H. H. Ding

Comput. Secur., 2021

A Novel and Dedicated Machine Learning Model for Malware Classification.

Miles Q. Li, Benjamin C. M. Fung, Philippe Charland, Steven H. H. Ding

Proceedings of the 16th International Conference on Software Technologies, 2021

A Novel Neural Network-Based Malware Severity Classification System.

Miles Q. Li, Benjamin C. M. Fung

Proceedings of the Software Technologies - 16th International Conference, 2021

2019

I-MAD: A Novel Interpretable Malware Detector Using Hierarchical Transformer.

Miles Q. Li, Benjamin C. M. Fung, Philippe Charland, Steven H. H. Ding

CoRR, 2019