Benjamin Fung

Membre académique associé

Professeur agrégé, McGill University, École des sciences de l'information

McGill University University

Sujets de recherche

Apprentissage automatique appliqué

Apprentissage de représentations

Apprentissage profond

Cybersécurité

Désinformation

Exploration des données

IA pour l'ingénierie logicielle

Recherche d'information

Vie privée

Site web

Google Scholar

Biographie

Benjamin Fung est titulaire d'une chaire de recherche du Canada en exploration de données pour la cybersécurité, professeur agrégé à l’École des sciences de l’information et membre agrégé de l’École d’informatique de l'Université McGill, rédacteur adjoint de IEEE Transactions of Knowledge and Data Engineering et rédacteur adjoint de Elsevier Sustainable Cities and Society (SCS). Il a obtenu un doctorat en informatique de l'Université Simon Fraser en 2007. Il a à son actif plus de 150 publications revues par un comité de lecture, et plus de 14 000 citations (h-index 57) qui couvrent les domaines de l'exploration de données, de l'apprentissage automatique, de la protection de la vie privée, de la cybersécurité et du génie du bâtiment. Ses travaux d'exploration de données dans les enquêtes criminelles et l'analyse de la paternité d’une œuvre ont été recensés par les médias du monde entier.

Publications

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

Charles E. Gagnon

Steven H. H. Ding

Philippe Charland

Benjamin Fung

Binary code similarity detection is a core task in reverse engineering. It supports malware analysis and vulnerability discovery by identify… (voir plus)ing semantically similar code in different contexts. Modern methods have progressed from manually engineered features to vector representations. Hand-crafted statistics (e.g., operation ratios) are interpretable, but shallow and fail to generalize. Embedding-based methods overcome this by learning robust cross-setting representations, but these representations are opaque vectors that prevent rapid verification. They also face a scalability-accuracy trade-off, since high-dimensional nearest-neighbor search requires approximations that reduce precision. Current approaches thus force a compromise between interpretability, generalizability, and scalability. We bridge these gaps using a language model-based agent to conduct structured reasoning analysis of assembly code and generate features such as input/output types, side effects, notable constants, and algorithmic intent. Unlike hand-crafted features, they are richer and adaptive. Unlike embeddings, they are human-readable, maintainable, and directly searchable with inverted or relational indexes. Without any matching training, our method respectively achieves 42% and 62% for recall@1 in cross-architecture and cross-optimization tasks, comparable to embedding methods with training (39% and 34%). Combined with embeddings, it significantly outperforms the state-of-the-art, demonstrating that accuracy, scalability, and interpretability can coexist.

2025-09-27

ArXiv (prépublication)

arxiv.org

Responsible AI Day

Ebrahim Bagheri

Faezeh Ensan

Calvin Hillis

Reihaneh Rabbany

Robin Cohen

Benjamin Fung

Sébastien Gambs

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

doi.org

Responsible AI Day

Ebrahim Bagheri

Faezeh Ensan

Calvin Hillis

Reihaneh Rabbany

Robin Cohen

Benjamin Fung

Sébastien Gambs

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

doi.org

Diminished social memory and hippocampal correlates of social interactions in chronic social defeat stress susceptibility

Amanda Larosa

Tian Rui Zhang

Alice S. Wong

Cyrus Y.H. Fung

Y. H. Fung Cyrus

Xiong Ling Yun (Jenny) Long

Prabhjeet Singh

Benjamin Fung

Tak Pan Wong

2025-01-30

Biological Psychiatry Global Open Science (publié)

doi.org

CSGraph2Vec: Distributed Graph-Based Representation Learning for Assembly Functions

Wael J. Alhashemi

Benjamin Fung

Adel Abusitta

Claude Fachkha

2024-11-06

2024 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) (publié)

doi.org

AugmenToxic: Leveraging Reinforcement Learning to Optimize LLM Instruction Fine-Tuning for Data Augmentation to Enhance Toxicity Detection

Arezo Bodaghi

Benjamin Fung

Ketra A. Schmitt

2024-10-29

ACM Transactions on the Web (publié)

doi.org

A Literature Review on Detecting, Verifying, and Mitigating Online Misinformation

Arezo Bodaghi

Ketra A. Schmitt

Pierre Watine

Benjamin Fung

Social media use has transformed communication and made social interaction more accessible. Public microblogs allow people to share and acce… (voir plus)ss news through existing and social-media-created social connections and access to public news sources. These benefits also create opportunities for the spread of false information. False information online can mislead people, decrease the benefits derived from social media, and reduce trust in genuine news. We divide false information into two categories: unintentional false information, also known as misinformation; and intentionally false information, also known as disinformation and fake news. Given the increasing prevalence of misinformation, it is imperative to address its dissemination on social media platforms. This survey focuses on six key aspects related to misinformation: 1) clarify the definition of misinformation to differentiate it from intentional forms of false information; 2) categorize proposed approaches to manage misinformation into three types: detection, verification, and mitigation; 3) review the platforms and languages for which these techniques have been proposed and tested; 4) describe the specific features that are considered in each category; 5) compare public datasets created to address misinformation and categorize into prelabeled content-only datasets and those including users and their connections; and 6) survey fact-checking websites that can be used to verify the accuracy of information. This survey offers a comprehensive and unprecedented review of misinformation, integrating various methodological approaches, datasets, and content-, user-, and network-based approaches, which will undoubtedly benefit future research in this field.

2024-08-01

IEEE Transactions on Computational Social Systems (publié)

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… (voir plus) 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 (publié)

doi.org

Survey on Explainable AI: Techniques, challenges and open issues

Adel Abusitta

Miles Q. Li

Benjamin Fung

2024-07-01

Expert systems with applications (publié)

doi.org

Survey on Explainable AI: Techniques, challenges and open issues

Adel Abusitta