The paper proposes a system-aware unsupervised framework that combines lightweight online detection with a contextual digital twin and LLM to provide interpretable, actionable anomaly diagnoses for In…

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…

IstGPT introduces a novel LLM-based framework for real-time, fine-grained anomaly detection in complex industrial cyber-physical systems, achieving state-of-the-art performance across multiple benchma…

The paper introduces i-SDT, an intelligent Self-Defending Digital Twin, which enhances cyber-physical security by accurately discriminating various attack types and maintaining safe operation without…

This paper demonstrates that an off-the-shelf Large Language Model (LLM) can function as a high-performing, explainable, human-in-the-loop layer for detecting cyberattacks in Industrial Control System…

This paper investigates the vulnerability of machine learning-based fault detection and localization systems in Cyber-Physical Systems (CPS) to backdoor attacks, demonstrating that such attacks are su…

The paper introduces a threat-oriented digital twinning methodology to enable reproducible and controllable cybersecurity evaluation of autonomous platforms, overcoming limitations in accessing real-w…

This paper discusses the significant challenges in developing a holistic intrusion detection system for Industrial Control Systems (ICS) that must cover all operational dimensions.

The paper proposes a structured prompt engineering framework to enhance the integrity and reliability of Chain-of-Thought (CoT) reasoning in LLMs, demonstrating significant improvements in security-se…

This paper introduces an attribution-driven analysis of encoder-based Large Language Models (LLMs) for network intrusion detection, demonstrating that the models make decisions based on meaningful tra…

The paper proposes a tool-mediated LLM architecture for autonomous cyber defense, formally proving its stability and demonstrating that it significantly reduces an attacker's expected payoff in real-w…

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…

The paper demonstrates a semantic denial-of-service attack against LLM-controlled robots by injecting short, safety-plausible phrases into the audio channel, causing the robot to halt or disrupt execu…

The paper provides a holistic threat model for LLM-enabled robotic systems by analyzing how conventional, adversarial, and conversational threats propagate across the entire perception-planning-actuat…

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…

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…

The paper proposes a declarative, autonomous, self-protecting framework for securing complex 5G/6G networks by leveraging a standardized security ontology and automated graph reasoning to neutralize l…

The paper proposes Dynamic Cyber Ranges, an advanced cyber range environment using LLM-driven Defender agents to counter the saturation of traditional security benchmarks, demonstrating that these dyn…

The paper proposes ExAI5G, a logic-based explainable AI framework that integrates a Transformer-based IDS with XAI techniques to provide highly accurate and transparent intrusion detection for 5G netw…

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…