This paper experimentally demonstrates the high detection performance of machine learning-based intrusion detection systems for identifying cyberattacks targeting OPC UA applications running over priv…

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 introduces Constraint-Guided Semantic Testing (ConSeT), a novel framework that systematically finds critical, pre-authentication vulnerabilities in 5G User Equipment (UE) by exploiting seman…

The paper investigates undetectable command and control (C2) channels within 5G core networks, demonstrating how compromised components can enable sophisticated attacks against subscriber security and…

This paper surveys information-theoretic approaches to secure Integrated Sensing and Communication (ISAC), providing a comprehensive review of models, security formulations, and fundamental limits.

The paper proposes a semi-automated framework that integrates network topology and vulnerability data to generate and analyze multi-step attack graphs in Industrial Control Systems, demonstrated using…

This paper provides the first comprehensive threat model for IoT-enabled Controlled Environment Agriculture (CEA) systems, identifying 123 unique threats and proposing a defense-in-depth framework to…

The paper demonstrates that standard homomorphic encryption (HE) schemes are insufficient to guarantee integrity in networked control systems (NCS) against covert attacks, proposing instead a verifiab…

The paper proposes StormShield, a fingerprint-based detection and mitigation technique implemented as an xApp on an O-RAN RIC, which effectively prevents gNB resource exhaustion caused by RRC signalin…

This paper demonstrates a non-disruptive, sidecar-based integration of NIST-standardized Post-Quantum Cryptography (PQC) into an open-source 5G core, showing that while it introduces a predictable lat…

This paper proposes a lightweight, multi-layer Machine Learning-based security framework for Industrial IoT (IIoT) to enhance trust convergence and detect advanced threats.

This paper surveys how integrating Edge Computing, Machine Learning, and Deep Learning can enhance the security and resilience of complex Internet of Vehicles (IoV) networks.

The paper analyzes the security and practical deployability of advanced Wi-Fi ranging standards (IEEE 802.11az/bk), concluding that while promising, secure implementation is highly sensitive to config…

The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…

The paper addresses the lack of independent measurement tools for modern mobile communication by designing and implementing open-source platforms to study cellular radio networks, operator services, a…

This paper demonstrates that side-channel attacks can be executed across chiplets within a package by repurposing communication-oriented interfaces as internal observation platforms, revealing informa…

The paper introduces PINSIGHT, a novel methodology that rigorously assesses Wi-Fi PIN code inference attacks by separating environmental effects from typing effects, concluding that current state-of-t…

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 Device Context Protocol (DCP) introduces a compact, safety-first communication standard designed to allow LLMs to reliably control resource-constrained physical microcontrollers, significantly imp…

The paper proposes Q-FE, a novel Quantum-Native 6G Far-Edge architecture that secures Industrial IoT Digital Twins by integrating micro-digital twins, compact post-quantum key exchange, and asynchrono…