arXiv subjects

Shutonu Mitra

Publications and source records attributed to Shutonu Mitra.

5 recordsLinked to original sources

arXiv2026

Assessing Socio-Cyber Vulnerability Using Survey and Social Media Data

The rapid growth of social media participation has increased exposure to socially engineered cyber threats (e.g., phishing, romance fraud, and tech-support scams), yet prevailing assessment tools remain fragmented: the Common Vulnerability Scoring System (CVSS) is primarily technical and largely omits human susceptibility, while the Social Vulnerability Index (SVI) is community-oriented and lacks cyber-specific modeling. To address this gap, we propose the Social Cyber Vulnerability Index (SCVI), an interpretable, uncertainty-aware metric combining two components: (i) an Individual Vulnerability Index (IVI), capturing awareness, behavior, psychological factors, and prior victimization, and (ii) an Attack Severity Index (ASI), capturing attack frequency, consequences, and sophistication. We validate SCVI across heterogeneous modalities: a nationally scoped survey (iPoll; 4,596 U.S. adults) and social-media narratives (450 Reddit r/scams reports, 2016-2024), demonstrating computation from both structured questionnaires and CI-driven feature extraction from text. Sensitivity analysis and 10,000-iteration Monte Carlo simulations show stable rankings under plausible weight variability and reveal context-dependent drivers. SCVI captures distinct socio-technical signals (Spearman correlation with CVSS $\rho = 0.33$; with SVI $\rho \approx -0.01$) and surfaces demographic and regional disparities. SCVI also provides substantially stronger separation between victim and non-victim groups than CVSS and SVI, supporting identification of high-risk populations and prioritization of interventions against emerging AI-enabled scams.

cs.SI↗

arXiv2025

Scam Shield: Multi-Model Voting and Fine-Tuned LLMs Against Adversarial Attacks

Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical Scam Detection System (HSDS) that combines a lightweight multi-model voting front end with a fine-tuned LLaMA 3.1 8B Instruct back end to improve accuracy and robustness against adversarial attacks. An ensemble of four classifiers provides preliminary predictions through majority vote, and ambiguous cases are escalated to the fine-tuned model, which is optimized with adversarial training to reduce misclassification. Experiments show that this hierarchical design both improves adversarial scam detection and shortens inference time by routing most cases away from the LLM, outperforming traditional machine-learning baselines and proprietary LLM baselines. The findings highlight the effectiveness of a hybrid voting mechanism and adversarial fine-tuning in fortifying LLMs against evolving scam tactics, enhancing the resilience of automated scam detection systems.

cs.CR↗

arXiv2025

SCVI: Bridging Social and Cyber Dimensions for Comprehensive Vulnerability Assessment

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 Vulnerability Index (SCVI), a novel framework integrating individual-level factors (e.g., awareness, behavioral traits, psychological attributes) and attack-level characteristics (e.g., frequency, consequence, sophistication) for comprehensive socio-cyber vulnerability assessment. SCVI is validated using survey data (iPoll) and textual data (Reddit scam reports), demonstrating adaptability across modalities while revealing demographic disparities and regional vulnerabilities. Comparative analyses with the Common Vulnerability Scoring System (CVSS) and the Social Vulnerability Index (SVI) show the superior ability of SCVI to capture nuanced socio-technical risks. Monte Carlo-based weight variability analysis confirms SCVI is robust and highlights its utility in identifying high-risk groups. By addressing gaps in traditional metrics, SCVI offers actionable insights for policymakers and practitioners, advancing inclusive strategies to mitigate emerging threats such as AI-powered phishing and deepfake scams.

cs.CR↗

arXiv2024

Enhancing Temporal Link Prediction with HierTKG: A Hierarchical Temporal Knowledge Graph Framework

The rapid spread of misinformation on social media, especially during crises, challenges public decision-making. To address this, we propose HierTKG, a framework combining Temporal Graph Networks (TGN) and hierarchical pooling (DiffPool) to model rumor dynamics across temporal and structural scales. HierTKG captures key propagation phases, enabling improved temporal link prediction and actionable insights for misinformation control. Experiments demonstrate its effectiveness, achieving an MRR of 0.9845 on ICEWS14 and 0.9312 on WikiData, with competitive performance on noisy datasets like PHEME (MRR: 0.8802). By modeling structured event sequences and dynamic social interactions, HierTKG adapts to diverse propagation patterns, offering a scalable and robust solution for real-time analysis and prediction of rumor spread, aiding proactive intervention strategies.

cs.SI↗

arXiv2024

Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance

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 detection. We addressed this issue by creating a comprehensive dataset with fine-grained labels of scam messages, including both original and adversarial scam messages. The dataset extended traditional binary classes for the scam detection task into more nuanced scam types. Our analysis showed how adversarial examples took advantage of vulnerabilities of a LLM, leading to high misclassification rate. We evaluated the performance of LLMs on these adversarial scam messages and proposed strategies to improve their robustness.

cs.CR↗