TL;DR
A Digital Twin (DT)-enabled IDS framework that combines deep learning with real-time process simulation is proposed that consistently outperforms the evaluated representative baseline IDS methods under identical experimental conditions.
A DT-based IDS that jointly models physical relationships among decoded powertrain signals and identifies attacks through residuals between predicted and observed behavior shows promise for detecting stealthy payload-level CAN attacks that preserve normal communication patterns, supporting behavior-based cybersecurity...
Experimental results demonstrate 91.52% accuracy and 91.76% Matthews Correlation coefficient (MCC), highlighting the effectiveness of the proposed approach, which outperforms existing techniques.
This work proposes a deep spiking Q-network-based intrusion detection system (DSQN-IDS) for the IIoT, formulating unknown intrusion detection as a Markov decision process (MDP), and employs a hierarchical multi-stage decision-making framework.
As MNI becomes increasingly vulnerable to new kinds of attacks from the cyber world, accurate and timely detection of intrusions becomes a primary key to the power of cybersecurity. More complex attack patterns, complex traffic interactions within large scales are not very collaborable with the typical signature-based...
A Markov-enhanced hybrid IDS that integrates physics-based modeling, data-driven anomaly detection, and statistical sequence analysis to secure a two-turbine cyber-physical wind farm, offering an analytically scalable architectural path toward more secure renewable energy infrastructures, while larger-farm empirical va...
Comparative analysis shows that ProvICS is among the few existing ICS/CPS benchmarks with multi-host kernel-level provenance, real PLC hardware-in-the-loop execution, decoded Modbus traffic, physical process-state measurements, and auxiliary raw PCAP traces in a time-synchronized collection.
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