1 citations · 2 across the 10 of their papers we have counts for
11 papers
Practical Auditing of Differentially Private Graph Release Mechanisms: Methodology, Toolkit, and Case Study
Nicholas D'Silva, Surya Nepal, Salil S. Kanhere
Differential Privacy (DP) has become a prominent approach for protecting graph data releases, yet empirical auditing methodologies have largely been developed for tabular datasets…
Does Teaming-Up LLMs Improve Secure Code Generation? A Comprehensive Evaluation with Multi-LLMSecCodeEval
Bushra Sabir, Shigang Liu, Seung Ick Jang +6
Automatically generating source code from natural language using large language models (LLMs) is becoming common, yet security vulnerabilities persist despite advances in fine tuni…
SoK: Practical Aspects of Releasing Differentially Private Graphs
Nicholas D'Silva, Surya Nepal, Salil S. Kanhere
Graph data is increasingly prevalent across domains, offering analytical value but raising significant privacy concerns. Edges may encode sensitive relationships, while node attrib…
Security in the Era of Perceptive Networks: A Comprehensive Taxonomic Framework for Integrated Sensing and Communication Security
Chandra Thapa, Surya Nepal
Integrated Sensing and Communication (ISAC) represents a significant shift in the 6G landscape, where wireless networks both sense the environment and communicate. While prior comp…
Future G Network's New Reality: Opportunities and Security Challenges
Chandra Thapa, Surya Nepal
Future G network's new reality is a widespread cyber-physical environment created by Integrated Sensing and Communication (ISAC). It is a crucial technology that transforms wireles…
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen +3
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable…
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning
Hassan Ali, Surya Nepal, Salil S. Kanhere +1
Recent works have shown that Federated Learning (FL) is vulnerable to backdoor attacks. Existing defenses cluster submitted updates from clients and select the best cluster for agg…
Honeyfile Camouflage: Hiding Fake Files in Plain Sight
Roelien C. Timmer, David Liebowitz, Surya Nepal +1
Honeyfiles are a particularly useful type of honeypot: fake files deployed to detect and infer information from malicious behaviour. This paper considers the challenge of naming ho…
Contextual Chart Generation for Cyber Deception
David D. Nguyen, David Liebowitz, Surya Nepal +2
Honeyfiles are security assets designed to attract and detect intruders on compromised systems. Honeyfiles are a type of honeypot that mimic real, sensitive documents, creating the…
Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions
David D. Nguyen, David Liebowitz, Surya Nepal +1
In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios…
AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity
Iqbal H. Sarker, Helge Janicke, Nazeeruddin Mohammad +2
This position paper explores the broad landscape of AI potentiality in the context of cybersecurity, with a particular emphasis on its possible risk factors with awareness, which c…