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Preprint
Aug 2026
AdaptoNet: Modular Foundation-Adaptive Neural Networks for Cyber-Physical Attack Detection in Power Grids
Evaluated across four IEEE test systems under in-region attacks blocking up to 20% of measurements, AdaptoNet recovers F1 from below 12\% to above 81\%, an approximate sevenfold improvement approaching the 89%-99% baseline with complete measurements.
Anissa Elias, Jennifer Rogers, Hui Lin et al.
· 0 citations
Digital Twin-Based Intrusion Detection for Vehicle Powertrain CAN Bus Systems
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...
Araf Rahman, M. Salek, Mashrur Chowdhury
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Jul 2026
Strategy Phasing of Cyber Attacks on Digital Substations
This paper proposes Substation Cyber Attack Strategy Phasing (SubCASP), a Hidden Markov Model(HMM)- based method that fuses IDS data logs to infer the current attack phase, next attack phase, and retrospective attack path.
Akila Herath, Chen-Ching Liu, Junho Hong et al.
· arXiv.org · 0 citations
Conference
Jul 2026
Federated Learning for Fault and False Data Injection Attack Detection in the IEEE 9-bus System
With the advent of the digitization of power grid systems, fault and cyber-attack detection have been a challenging problem in the field. Due to stealthy cyber-attacks and their similar effects on the grid’s voltage and frequency, it is increasingly difficult for statistical methods to distinguish between faults and cy...
Najla M. Aljuaid, D. Kushwaha, Roghieh Abdollahi et al.
· International Conference on... · 0 citations
Aug 2026
Resilient control and Markov-enhanced hybrid multi-feature intrusion detection for cyber-physical wind farms under SCADA delays and coordinated cyber-attacks
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...
Mahdi Esmaeelihesari, M. Davoudi, N. Pariz
· International Journal of Dyn... · 0 citations
Open access
Aug 2026
A statistically defensible machine learning pipeline for FDIA detection and resilience evaluation in renewable smart grids
The results support the feasibility of leakage-safe machine-learning evaluation for FDIA detection under the simulated renewable smart-grid setting and provide additional support for evaluation transparency.
Abdulrahman Almazroui, F. Albeladi, R. Almazmomi
· Scientific Reports · 0 citations