The paper proposes INTARG, an informed and selective adversarial attack framework for time-series forecasting that significantly increases prediction error by targeting only the most vulnerable time s…

This paper proposes a gap-prioritization framework to bridge the gap between theoretical cyber attack prediction research and practical operational deployment by identifying critical implementation hu…

The paper proposes a dynamic queueing framework that estimates an organization's cyber resources and attack surface dynamics by analyzing the timestamps of vulnerabilities and fixes, achieving high ac…

This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept dri…

The paper empirically evaluates domain-adapted and general-purpose LLMs for structured threat modelling (STRIDE on 5G security), finding that domain adaptation and model size do not guarantee reliable…

TIBlender is a multi-agent system that integrates fragmented cyber threat signals from multiple social media platforms to generate comprehensive, actionable threat intelligence reports, significantly…

The paper proposes MVRAF, a data-driven framework that quantifies vulnerability risk in large-scale cloud infrastructure by integrating multiple attack attributes and analyzing cumulative risk distrib…

The paper analyzes critical vulnerabilities (CVSS >= 9) using a mixed-methods approach, finding that systemic delays in patch deployment and remediation persist despite improved disclosure.

The paper argues that zero-day attacks primarily exploit undisclosed vulnerabilities rather than exhibiting novel behaviors, advocating for vulnerability-centric detection methods over purely behavior…

The paper introduces a queueing-theoretic framework to model dynamic cyber-attack surfaces, developing an adaptive reinforcement learning defense policy that significantly reduces active vulnerabiliti…

The paper introduces STRIDE-AI, a novel threat modeling framework that adapts classical STRIDE for generative AI, successfully reducing the attack success rate of a tested LLM chatbot from 80% to 15%.

The paper introduces ForesightFlow, an Information Leakage Score (ILS) framework, to quantify pre-event information leakage in prediction markets, and proposes a necessary extension to analyze empiric…

The paper proposes SAGE, a framework that uses Signal-Amplified Guided Embeddings to overcome 'Signal Submersion' in LLMs, significantly boosting vulnerability detection accuracy across multiple progr…

The paper proposes an automated, LLM-enabled threat hunting framework integrated with Splunk to help SOC analysts autonomously monitor evolving threats and prioritize suspicious network traffic.

The paper proposes a novel semi-automated method to perform continuous threat modeling by inferring the actual system architecture from combined static configuration and dynamic network flow data, sig…

The paper details a data science competition focused on identifying hidden backdoor triggers (trojan horses) in deep forecasting models used for critical space operations.

The paper proposes a fuzzy modeling framework using subnormal Gaussian fuzzy numbers to prioritize IDS alerts by explicitly incorporating threat severity, detection confidence, and organizational risk…

VulGD is a dynamic, open-access graph database that aggregates cybersecurity data from multiple sources and uses LLM embeddings to improve vulnerability representation and risk assessment.

PARD-SSM is a probabilistic framework that models network traffic as a switching state-space system to detect multi-stage cyber-attacks in real-time with high accuracy and predictive capability.

The paper introduces AI-native asset intelligence, a framework that structures heterogeneous security data into a consistent, contextual layer for proactive, stable, and accurate asset-level risk prio…