This paper systematically reviews airport cybersecurity threats, mapping them to the MITRE ATT&CK Matrix to provide actionable recommendations for modern defense models like Zero Trust.

The paper presents an approach to automatically generate a large number of diverse and complex cybersecurity scenarios that model enterprise IT systems for training purposes.

The paper introduces STRIKE, a multi-dimensional structured taxonomy designed to provide a comprehensive and unified framework for classifying the rapidly evolving complexity of modern cybercrimes.

The paper argues that agent security must be treated as a systems problem, requiring the enforcement of security invariants at the system level rather than solely relying on improving the underlying A…

The paper introduces LITE-SOC, a lightweight, web-based simulator designed to provide a practical, accessible alternative for teaching cybersecurity SOC workflows without requiring complex, expensive…

This paper reviews current trends in AI-based cybersecurity, specifically analyzing various AI techniques applied to intrusion detection to provide comparative insights.

This paper systematically analyzes 123 publications on anti-forensics to quantify techniques and attack vectors, identify research patterns, and propose directions for a more coherent and ethical unde…

SMSI is a novel neuro-symbolic pipeline that automates threat modeling for cyber-physical systems by generating a prioritized list of NIST 800-53 security controls directly from a SysML architecture m…

HySecTwin introduces a knowledge-driven digital twin framework that uses semantic modeling and hybrid reasoning to provide explainable, context-aware, and high-speed threat detection for complex Cyber…

This paper proposes a Security Decision Support System that recommends security control sub-families using a curated dataset and multi-agent model.

This paper analyzes high-impact Web3 security incidents to show that most losses stem from off-chain organizational and operational failures, not just smart contract bugs.

The paper proposes CyberAId, a hybrid multi-agent system designed to enhance cybersecurity for financial institutions by integrating specialized LLM subagents with existing SIEM/XDR telemetry, address…

The paper forecasts that agentic AI will compress the cyber attack lifecycle by lowering the cost of multiple attack stages, necessitating immediate operational security upgrades for enterprises and t…

The paper introduces the Canonical Security Telemetry Substrate (CSTS), a standardized, AI-ready foundation designed to harmonize fragmented and heterogeneous cybersecurity data into a unified model f…

The paper develops a novel, resource-aware cybersecurity risk assessment framework specifically tailored for power-limited CubeSat missions, demonstrating that adapting controls can significantly impr…

The paper introduces XekRung, a frontier large language model for cybersecurity, which achieves state-of-the-art performance on domain-specific benchmarks through a comprehensive training and evaluati…

This survey comprehensively analyzes the IoT threat landscape by detailing 28 common attacks and mapping them to foundational vulnerability classes, providing a structured roadmap for building secure…

The paper introduces ASTRAL, a multimodal LLM-driven framework that reconstructs and analyzes fragmented cyber-physical system architectures to enable comprehensive and quantitative security risk asse…

This paper proposes a hybrid CNN-LSTM framework to enhance cyber attack detection and prevention in U.S. critical digital infrastructure by evaluating multiple machine learning models on the CSE-CIC-I…