SCVI: Bridging Social and Cyber Dimensions for Comprehensive Vulnerability Assessment

2025-03-24

AI Summary

Existing social network vulnerability metrics inadequately capture socio-technical risks, failing to jointly model human and technical dimensions. Method: We propose the Social Cyber Vulnerability Index (SCVI), the first unified metric integrating individual-level factors (security awareness, behavioral patterns, psychological attributes) with attack-level characteristics (occurrence frequency, impact severity, technical sophistication). SCVI is constructed by fusing iPoll survey data and Reddit textual corpora; attack features are extracted via NLP, and a weighted composite index is derived. Weight sensitivity is rigorously validated using Monte Carlo analysis. Results: Experiments demonstrate that SCVI outperforms CVSS and SVI in accurately characterizing socio-technical risk. It uncovers systematic disparities across demographic groups and geographic regions, identifies high-risk populations and locales, and delivers actionable insights for inclusive policy design and platform-level security interventions.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Socio-technical web-based systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Assessing social cyber vulnerabilities on platforms
• Integrating individual and attack factors for risk evaluation
• Validating a novel framework with real-world data

Innovation

• Integrates individual and attack-level factors
• Validated with survey and textual data
• Robust Monte Carlo weight analysis