5 papers
X-MAP: eXplainable Misclassification Analysis and Profiling for Spam and Phishing Detection
Qi Zhang, Dian Chen, Lance M. Kaplan +4
Misclassifications in spam and phishing detection are very harmful, as false negatives expose users to attacks while false positives degrade trust. Existing uncertainty-based detec…
Beyond Binary Opinions: A Deep Reinforcement Learning-Based Approach to Uncertainty-Aware Competitive Influence Maximization
Qi Zhang, Dian Chen, Lance M. Kaplan +4
The Competitive Influence Maximization (CIM) problem involves multiple entities competing for influence in online social networks (OSNs). While Deep Reinforcement Learning (DRL) ha…
SCVI: Bridging Social and Cyber Dimensions for Comprehensive Vulnerability Assessment
Shutonu Mitra, Tomas Neguyen, Qi Zhang +8
The rise of cyber threats on social media platforms necessitates advanced metrics to assess and mitigate social cyber vulnerabilities. This paper presents the Social Cyber Vulnerab…
Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance
Chen-Wei Chang, Shailik Sarkar, Shutonu Mitra +7
Can we trust Large Language Models (LLMs) to accurately predict scam? This paper investigates the vulnerabilities of LLMs when facing adversarial scam messages for the task of scam…
Winning the Social Media Influence Battle: Uncertainty-Aware Opinions to Understand and Spread True Information via Competitive Influence Maximization
Qi Zhang, Lance M. Kaplan, Audun Jøsang +3
Competitive Influence Maximization (CIM) involves entities competing to maximize influence in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in CI…