17 citations · 60 across the 24 of their papers we have counts for
24 papers
LLMs in the SOC: An Empirical Study of Human-AI Collaboration in Security Operations Centres
Ronal Singh, Shahroz Tariq, Fatemeh Jalalvand +4
The integration of Large Language Models (LLMs) into Security Operations Centres (SOCs) presents a transformative, yet still evolving, opportunity to reduce analyst workload throug…
ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal +1
Modern Security Operations Centres (SOCs) integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, an…
Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity
Shahroz Tariq, Ronal Singh, Mohan Baruwal Chhetri +2
This study investigates whether large language models (LLMs) can function as intelligent collaborators to bridge expertise gaps in cybersecurity decision-making. We examine two rep…
Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure
Zehang Deng, Ruoxi Sun, Minhui Xue +4
AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of soci…
A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams
Shahroz Tariq, Mohan Baruwal Chhetri, Surya Nepal +1
This paper introduces A2C, a multi-stage collaborative decision framework designed to enable robust decision-making within human-AI teams. Drawing inspiration from concepts such as…
Reducing Adversarial Training Cost with Gradient Approximation
Huihui Gong
Deep learning models have achieved state-of-the-art performances in various domains, while they are vulnerable to the inputs with well-crafted but small perturbations, which are na…
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Huihui Gong, Minjing Dong, Siqi Ma +3
Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single ty…
Digital Twins and the Future of their Use Enabling Shift Left and Shift Right Cybersecurity Operations
Ahmad Mohsin, Helge Janicke, Surya Nepal +1
Digital Twins (DTs), optimize operations and monitor performance in Smart Critical Systems (SCS) domains like smart grids and manufacturing. DT-based cybersecurity solutions are in…
DeepTheft: Stealing DNN Model Architectures through Power Side Channel
Yansong Gao, Huming Qiu, Zhi Zhang +6
Deep Neural Network (DNN) models are often deployed in resource-sharing clouds as Machine Learning as a Service (MLaaS) to provide inference services.To steal model architectures t…
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization
Huihui Gong, Minjing Dong, Siqi Ma +3
Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking…
Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children's Apps
Ruoxi Sun, Minhui Xue, Gareth Tyson +3
The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world's most marginalized children. However, while legislatures a…
Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection
Chuyang Zhou, Jiajun Huang, Daochang Liu +4
Facial forgery detection is a crucial but extremely challenging topic, with the fast development of forgery techniques making the synthetic artefact highly indistinguishable. Prior…
Anti-Compression Contrastive Facial Forgery Detection
Jiajun Huang, Xinqi Zhu, Chengbin Du +3
Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, espe…
Icicle: A Re-Designed Emulator for Grey-Box Firmware Fuzzing
Michael Chesser, Surya Nepal, Damith C. Ranasinghe
Emulation-based fuzzers enable testing binaries without source code, and facilitate testing embedded applications where automated execution on the target hardware architecture is d…
Masked Vector Quantization
David D. Nguyen, David Leibowitz, Surya Nepal +1
Generative models with discrete latent representations have recently demonstrated an impressive ability to learn complex high-dimensional data distributions. However, their perform…
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks
Seonhye Park, Alsharif Abuadbba, Shuo Wang +4
Training deep neural networks (DNNs) requires large datasets and powerful computing resources, which has led some owners to restrict redistribution without permission. Watermarking…
Reconstruction Attack on Differential Private Trajectory Protection Mechanisms
Erik Buchholz, Alsharif Abuadbba, Shuo Wang +2
Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the…
Unraveling Threat Intelligence Through the Lens of Malicious URL Campaigns
Mahathir Almashor, Ejaz Ahmed, Benjamin Pick +6
The daily deluge of alerts is a sombre reality for Security Operations Centre (SOC) personnel worldwide. They are at the forefront of an organisation's cybersecurity infrastructure…
Profiler: Profile-Based Model to Detect Phishing Emails
Mariya Shmalko, Alsharif Abuadbba, Raj Gaire +3
Email phishing has become more prevalent and grows more sophisticated over time. To combat this rise, many machine learning (ML) algorithms for detecting phishing emails have been…
Deception for Cyber Defence: Challenges and Opportunities
David Liebowitz, Surya Nepal, Kristen Moore +6
Deception is rapidly growing as an important tool for cyber defence, complementing existing perimeter security measures to rapidly detect breaches and data theft. One of the factor…
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models
Shuo Wang, Sharif Abuadbba, Sidharth Agarwal +6
Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model para…
Local Differential Privacy for Federated Learning
M. A. P. Chamikara, Dongxi Liu, Seyit Camtepe +4
Advanced adversarial attacks such as membership inference and model memorization can make federated learning (FL) vulnerable and potentially leak sensitive private data. Local diff…
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors
Ruoxi Sun, Minhui Xue, Gareth Tyson +6
Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection,…
Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?
Jihyeon Ryu, Yifeng Zheng, Yansong Gao +6
Collaborative inference has recently emerged as an attractive framework for applying deep learning to Internet of Things (IoT) applications by splitting a DNN model into several su…