Portrait de Benjamin Fung

Benjamin Fung

Membre académique associé
Professeur agrégé, McGill University, École des sciences de l'information
Sujets de recherche
Exploration des données
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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

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.
2023-01-01
IEEE Transactions on Computational Social Systems (publié)
doi.org
A Novel Deep Multi-head Attentive Vulnerable Line Detector
Miles Q. Li
Benjamin Fung
Ashita Diwan
2023-01-01
Procedia Computer Science (publié)
doi.org
Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
Jwen Fai Low
Benjamin Fung
Farkhund Iqbal
COVID-19 is an opportunity to study public acceptance of a “new” healthcare intervention, universal masking, which unlike vaccination, i… (voir plus)s mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, and controversiality and disagreeableness, represented by ratios of the aforementioned counts, favored pro-mask tweets initially but with anti-mask tweets slowly gaining ground. Additional analysis raised the possibility of the platform owners suppressing certain parts of the mask-wearing discussion.
2023-01-01
IEEE Access (publié)
doi.org
The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions
Salwa Razaulla
Claude Fachkha
Christine Markarian
Amjad Gawanmeh
Wathiq Mansoor
Benjamin Fung
Chadi Assi
The proliferation of ransomware has become a significant threat to cybersecurity in recent years, causing significant financial, reputationa… (voir plus)l, and operational damage to individuals and organizations. This paper aims to provide a comprehensive overview of the evolution of ransomware, its taxonomy, and its state-of-the-art research contributions. We begin by tracing the origins of ransomware and its evolution over time, highlighting the key milestones and major trends. Next, we propose a taxonomy of ransomware that categorizes different types of ransomware based on their characteristics and behavior. Subsequently, we review the existing research over several years in regard to detection, prevention, mitigation, and prediction techniques. Our extensive analysis, based on more than 150 references, has revealed that significant research, specifically 72.8%, has focused on detecting ransomware. However, a lack of emphasis has been placed on predicting ransomware. Additionally, of the studies focused on ransomware detection, a significant portion, 70%, have utilized Machine Learning methods. This study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware, and two issues specific to Machine Learning models. Adversarial machine learning exploitation and concept drift have been identified as under-researched areas in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.
2023-01-01
IEEE Access (publié)
doi.org
In-Processing Fairness Improvement Methods for Regression Data-Driven Building Models: Achieving Uniform Energy Prediction
Ying Sun
Benjamin Fung
Fariborz Haghighat
2022-12-01
Energy and Buildings (publié)
doi.org
A Multifaceted Framework to Evaluate Evasion, Content Preservation, and Misattribution in Authorship Obfuscation Techniques
Malik H. Altakrori
Thomas Scialom
Benjamin Fung
Jackie Cheung
2022-12-01
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
VDGraph2Vec: Vulnerability Detection in Assembly Code using Message Passing Neural Networks
Ashita Diwan
Miles Q. Li
Benjamin Fung
Software vulnerability detection is one of the most challenging tasks faced by reverse engineers. Recently, vulnerability detection has rece… (voir plus)ived a lot of attention due to a drastic increase in the volume and complexity of software. Reverse engineering is a time-consuming and labor-intensive process for detecting malware and software vulnerabilities. However, with the advent of deep learning and machine learning, it has become possible for researchers to automate the process of identifying potential security breaches in software by developing more intelligent technologies. In this research, we propose VDGraph2Vec, an automated deep learning method to generate representations of assembly code for the task of vulnerability detection. Previous approaches failed to attend to topological characteristics of assembly code while discovering the weakness in the software. VDGraph2Vec embeds the control flow and semantic information of assembly code effectively using the expressive capabilities of message passing neural networks and the RoBERTa model. Our model is able to learn the important features that help distinguish between vulnerable and non-vulnerable software. We carry out our experimental analysis for performance benchmark on three of the most common weaknesses and demonstrate that our model can identify vulnerabilities with high accuracy and outperforms the current state-of-the-art binary vulnerability detection models.
2022-12-01
International Conference on Machine Learning and Applications (publié)
doi.org
Towards Adaptive Cybersecurity for Green IoT
Talal Halabi
Martine Bellaiche
Benjamin Fung
The Internet of Things (IoT) paradigm has led to an explosion in the number of IoT devices and an exponential rise in carbon footprint incur… (voir plus)red by overburdened IoT networks and pervasive cloud/edge communications. Hence, there is a growing interest in industry and academia to enable the efficient use of computing infrastructures by optimizing the management of data center and IoT resources (hardware, software, network, and data) and reducing operational costs to slash greenhouse gas emissions and create healthy environments. Cybersecurity has also been considered in such efforts as a contributor to these environmental issues. Nonetheless, most green security approaches focus on designing low-overhead encryption schemes and do not emphasize energy-efficient security from architectural and deployment viewpoints. This paper sheds light on the emerging paradigm of adaptive cybersecurity as one of the research directions to support sustainable computing in green IoT. It presents three potential research directions and their associated methods for designing and deploying adaptive security in green computing and resource-constrained IoT environments to save on energy consumption. Such efforts will transform the development of data-driven IoT security solutions to be greener and more environment-friendly.
2022-11-24
2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) (publié)
doi.org
The generalizability of pre-processing techniques on the accuracy and fairness of data-driven building models: a case study
Ying Sun
Benjamin Fung
Fariborz Haghighat
2022-08-01
Energy and Buildings (publié)
doi.org
H4rm0ny: A Competitive Zero-Sum Two-Player Markov Game for Multi-Agent Learning on Evasive Malware Generation and Detection
Christopher Molloy
Steven H. H. Ding
Benjamin Fung
Philippe Charland
To combat the increasingly versatile and mutable modern malware, Machine Learning (ML) is now a popular and effective complement to the exis… (voir plus)ting signature-based techniques for malware triage and identification. However, ML is also a readily available tool for adversaries. Recent studies have shown that malware can be modified by deep Reinforcement Learning (RL) techniques to bypass AI-based and signature-based anti-virus systems without altering their original malicious functionalities. These studies only focus on generating evasive samples and assume a static detection system as the enemy.Malware detection and evasion essentially form a two-party cat-and-mouse game. Simulating the real-life scenarios, in this paper we present the first two-player competitive game for evasive malware detection and generation, following the zero-sum Multi-Agent Reinforcement Learning (MARL) paradigm. Our experiments on recent malware show that the produced malware detection agent is more robust against adversarial attacks. Also, the produced malware modification agent is able to generate more evasive samples fooling both AI-based and other anti-malware techniques.
2022-07-27
Computer Science Symposium in Russia (publié)
doi.org
On the Effectiveness of Interpretable Feedforward Neural Network
Miles Q. Li
Benjamin Fung
Adel Abusitta
Deep learning models have achieved state-of-the-art performance in many classification tasks. However, most of them cannot provide an explan… (voir plus)ation for their classification results. Machine learning models that are interpretable are usually linear or piecewise linear and yield inferior performance. Non-linear models achieve much better classification performance, but it is usually hard to explain their classification results. As a counter-example, an interpretable feedforward neural network (IFFNN) is proposed to achieve both high classification performance and interpretability for malware detection. If the IFFNN can perform well in a more flexible and general form for other classification tasks while providing meaningful explanations, it may be of great interest to the applied machine learning community. In this paper, we propose a way to generalize the interpretable feedforward neural network to multi-class classification scenarios and any type of feedforward neural networks, and evaluate its classification performance and interpretability on interpretable datasets. We conclude by finding that the generalized IFFNNs achieve comparable classification performance to their normal feedforward neural network counterparts and provide meaningful explanations. Thus, this kind of neural network architecture has great practical use.
2022-07-18
2022 International Joint Conference on Neural Networks (IJCNN) (publié)
doi.org
arxiv.org
Incentivized Security-Aware Computation Offloading for Large-Scale Internet of Things Applications
Talal Halabi
Adel Abusitta
Glaucio H.S. Carvalho
Benjamin Fung
2022-07-05
2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) (publié)
doi.org