79 citations · 167 across the 17 of their papers we have counts for
18 papers
SoK: How Frontier AI Reshapes System-Level Security Risk Dynamics in Critical Infrastructure
Chandra Thapa, Mohan Baruwal Chhetri, Marthie Grobler +2
Frontier artificial intelligence (FAI), encompassing large-scale, general-purpose AI systems, including large language models, multimodal foundation models, and agentic systems, is…
From Frontier to Shadow AI: A Simmering Threat to Assurance and Security in Critical Infrastructure
Mohan Baruwal Chhetri, Shahroz Tariq, Tooba Aamir +3
Frontier AI systems, including large language models and emerging agentic AI tools, offer significant operational benefits but present unique challenges to critical infrastructure…
From misinformation to climate crisis: Navigating vulnerabilities in the cyber-physical-social systems
Tooba Aamir, Marthie Grobler, Giovanni Russello
Within the cyber-physical-social-climate nexus, all systems are deeply interdependent: cyber infrastructure facilitates communication, data processing, and automation across physic…
Deep Learning-Based Out-of-distribution Source Code Data Identification: How Far Have We Gone?
Van Nguyen, Xingliang Yuan, Tingmin Wu +3
Software vulnerabilities (SVs) have become a common, serious, and crucial concern to safety-critical security systems. That leads to significant progress in the use of AI-based met…
An Innovative Information Theory-based Approach to Tackle and Enhance The Transparency in Phishing Detection
Van Nguyen, Tingmin Wu, Xingliang Yuan +3
Phishing attacks have become a serious and challenging issue for detection, explanation, and defense. Despite more than a decade of research on phishing, encompassing both technica…
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches
Reena Zelenkova, Jack Swallow, M. A. P. Chamikara +5
Biometric data, such as face images, are often associated with sensitive information (e.g medical, financial, personal government records). Hence, a data breach in a system storing…
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…
Systematic Literature Review on Cyber Situational Awareness Visualizations
Liuyue Jiang, Asangi Jayatilaka, Mehwish Nasim +3
The dynamics of cyber threats are increasingly complex, making it more challenging than ever for organizations to obtain in-depth insights into their cyber security status. Therefo…
Robust Training Using Natural Transformation
Shuo Wang, Lingjuan Lyu, Surya Nepal +3
Previous robustness approaches for deep learning models such as data augmentation techniques via data transformation or adversarial training cannot capture real-world variations th…
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
Shuo Wang, Surya Nepal, Kristen Moore +3
The diversity and quantity of data warehouses, gathering data from distributed devices such as mobile devices, can enhance the success and robustness of machine learning algorithms…
Adversarial Defense by Latent Style Transformations
Shuo Wang, Surya Nepal, Alsharif Abuadbba +2
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack…
Defending Adversarial Attacks via Semantic Feature Manipulation
Shuo Wang, Tianle Chen, Surya Nepal +3
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and…
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Shuo Wang, Tianle Chen, Shangyu Chen +3
Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as…
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
Shuo Wang, Surya Nepal, Carsten Rudolph +3
Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teac…
Generating Semantic Adversarial Examples via Feature Manipulation
Shuo Wang, Surya Nepal, Carsten Rudolph +3
The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wi…
A model for system developers to measure the privacy risk of data
Awanthika Senarath, Marthie Grobler, Nalin Asanka Gamagedara Arachchilage
In this paper, we propose a model that could be used by system developers to measure the privacy risk perceived by users when they disclose data into software systems. We first der…
Security and Performance Considerations in ROS 2: A Balancing Act
Jongkil Kim, Jonathon M. Smereka, Calvin Cheung +2
Robot Operating System (ROS) 2 is a ground-up re-design of ROS 1 to support performance critical cyber-physical systems (CPSs) using the Data Distribution Service (DDS) middleware.…
Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps
Tingmin Wu, Lihong Tang, Rongjunchen Zhang +5
Android users are increasingly concerned with the privacy of their data and security of their devices. To improve the security awareness of users, recent automatic techniques produ…