Portrait of Benjamin Fung

Benjamin Fung

Associate Academic Member
Associate Professor, McGill University, School of Information Studies
Research Topics
Data Mining
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Biography

Benjamin Fung is a Canada Research Chair in Data Mining for Cybersecurity, as well as a full professor at the School of Information Studies and associate member of the School of Computer Science, McGill University.

Fung serves as an associate editor of IEEE Transactions of Knowledge and Data Engineering and Sustainable Cities and Society. He received his PhD in computing science from Simon Fraser University in 2007.

Dr. Fung has over 150 refereed publications to his credit and and more than 14,000 citations (h-index 57) spanning the fields of data mining, machine learning, privacy, cybersecurity and building engineering. His findings in the fields of data mining for crime investigations and authorship analysis have been reported by the media worldwide.

Publications

Diminished social memory and hippocampal correlates of social interactions in chronic social defeat stress susceptibility
Amanda Larosa
Tian Rui Zhang
Alice S. Wong
Y. H. Fung Cyrus
Xiong Ling Yun (Jenny) Long
Benjamin Fung
Tak Pan Wong
2024-08-08
bioRxiv (preprint)
doi.org
A Comprehensive Analysis of Explainable AI for Malware Hunting
Mohd Saqib
Samaneh Mahdavifar
Benjamin Fung
Philippe Charland
In the past decade, the number of malware variants has increased rapidly. Many researchers have proposed to detect malware using intelligent… (see more) techniques, such as Machine Learning (ML) and Deep Learning (DL), which have high accuracy and precision. These methods, however, suffer from being opaque in the decision-making process. Therefore, we need Artificial Intelligence (AI)-based models to be explainable, interpretable, and transparent to be reliable and trustworthy. In this survey, we reviewed articles related to Explainable AI (XAI) and their application to the significant scope of malware detection. The article encompasses a comprehensive examination of various XAI algorithms employed in malware analysis. Moreover, we have addressed the characteristics, challenges, and requirements in malware analysis that cannot be accommodated by standard XAI methods. We discussed that even though Explainable Malware Detection (EMD) models provide explainability, they make an AI-based model more vulnerable to adversarial attacks. We also propose a framework that assigns a level of explainability to each XAI malware analysis model, based on the security features involved in each method. In summary, the proposed project focuses on combining XAI and malware analysis to apply XAI models for scrutinizing the opaque nature of AI systems and their applications to malware analysis.
2024-07-11
ACM Computing Surveys (published)
doi.org
AsmDocGen: Generating Functional Natural Language Descriptions for Assembly Code
Jesia Yuki
Mohammadhossein Amouei
Benjamin Fung
Philippe Charland
Andrew Walenstein
2024-07-08
Proceedings of the 19th International Conference on Software Technologies (published)
doi.org
Better entity matching with transformers through ensembles
Jwen Fai Low
Benjamin Fung
Pulei Xiong
2024-06-01
Knowledge-Based Systems (published)
doi.org
GAGE: Genetic Algorithm-Based Graph Explainer for Malware Analysis
Mohd Saqib
Benjamin Fung
Philippe Charland
Andrew Walenstein
Malware analysts often prefer reverse engineering using Call Graphs, Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), which involves… (see more) the utilization of black-box Deep Learning (DL) models. The proposed research introduces a structured pipeline for reverse engineering-based analysis, offering promising results compared to state-of-the-art methods and providing high-level interpretability for malicious code blocks in subgraphs. We propose the Canonical Executable Graph (CEG) as a new representation of Portable Executable (PE) files, uniquely incorporating syntactical and semantic information into its node embeddings. At the same time, edge features capture structural aspects of PE files. This is the first work to present a PE file representation encompassing syntactical, semantic, and structural characteristics, whereas previous efforts typically focused solely on syntactic or structural properties. Furthermore, recognizing the limitations of existing graph explanation methods within Explainable Artificial Intelligence (XAI) for malware analysis, primarily due to the specificity of malicious files, we introduce Genetic Algorithm-based Graph Explainer (GAGE). GAGE operates on the CEG, striving to identify a precise subgraph relevant to predicted malware families. Through experiments and comparisons, our proposed pipeline exhibits substantial improvements in model robustness scores and discriminative power compared to the previous benchmarks. Furthermore, we have successfully used GAGE in practical applications on real-world data, producing meaningful insights and interpretability. This research offers a robust solution to enhance cybersecurity by delivering a transparent and accurate understanding of malware behaviour. Moreover, the proposed algorithm is specialized in handling graph-based data, effectively dissecting complex content and isolating influential nodes.
2024-05-13
IEEE International Conference on Data Engineering (published)
doi.org
Fairness-aware data-driven-based model predictive controller: A study on thermal energy storage in a residential building
Ying Sun
Fariborz Haghighat
Benjamin Fung
2024-05-01
Journal of Energy Storage (published)
doi.org
Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection
Litao Li
Steven H. H. Ding
Andrew Walenstein
Philippe Charland
Benjamin Fung
2024-04-03
ArXiv (preprint)
doi.org
arxiv.org
Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media
Jwen Fai Low
Benjamin Fung
Farkhund Iqbal
Claude Fachkha
2024-02-02
ArXiv (preprint)
doi.org
arxiv.org
BETAC: Bidirectional Encoder Transformer for Assembly Code Function Name Recovery
Guillaume Breyton
Mohd Saqib
Benjamin Fung
Philippe Charland
Recovering function names from stripped binaries is a crucial and time-consuming task for software reverse engineering’ particularly in en… (see more)hancing network reliability, resilience, and security. This paper tackles the challenge of recovering function names in stripped binaries, a fundamental step in reverse engineering. The absence of syntactic information and the possibility of different code producing identical behavior complicate this task. To overcome these challenges, we introduce a novel model, the Bidirectional Encoder Transformer for Assembly Code (BETAC), leveraging a transformer-based architecture known for effectively processing sequential data. BETAC utilizes self-attention mechanisms and feed-forward networks to discern complex relationships within assembly code for precise function name prediction. We evaluated BETAC against various existing encoder and decoder models in diverse binary datasets, including benign and malicious codes in multiple formats. Our model demonstrated superior performance over previous techniques in certain metrics and showed resilience against code obfuscation.
2024-01-01
DRCN (published)
doi.org
Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation.
Da-Wei Jaw
Shih-Chia Huang
Zhihui Lu
Benjamin Fung
Sy-Yen Kuo
Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with suffic… (see more)ient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low-and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low-and sufficient-luminance domains.
2024-01-01
IEEE Trans. Cybern. (published)
doi.org
Survey on Explainable AI: Techniques, challenges and open issues
Adel Abusitta
Miles Q. Li
Benjamin Fung
2024-01-01
Expert Syst. Appl. (published)
doi.org
Technological Solutions to Online Toxicity: Potential and Pitfalls
Arezo Bodaghi
Benjamin Fung
Ketra A. Schmitt
Social media platforms present a perplexing duality, acting at once as sites to build community and a sense of belonging, while also giving … (see more)rise to misinformation, facilitating and intensifying disinformation campaigns and perpetuating existing patterns of discrimination from the physical world. The first-step platforms take in mitigating the harmful side of social media involves identifying and managing toxic content. Users produce an enormous volume of posts which must be evaluated very quickly. This is an application context that requires machine-learning (ML) tools, but as we detail in this article, ML approaches rely on human annotators, analysts, and moderators. Our review of existing methods and potential improvements indicates that neither humans nor ML can be removed from this process in the near future. However, we see room for improvement in the working conditions of these human workers.
2023-12-01
IEEE Technology and Society Magazine (published)
doi.org