Citation Attack Graph
Presekal et al.  developed a new technique for online cyberattack awareness. This technique improves the power grid resilience and assists the power system operators during localization and identification processes of the active attack locations in the Operational Technology (OT) network on a real-time basis. The presented technique used a hybrid DL mechanism, i.e., deep convolutional network with Graph Convolutional LSTM (GC-LSTM), for time-series classification-related AD.
Spoofing, Sniffing, and Message Relay –
A. Presekal et al.11 developed a hybrid deep learning method for making an attack graph for cyber-physical power systems (CPS). It helps secure CPS by identifying possible cyberattacks and weak spots.
(iii) trigger loss of control over the energy system through the distributed denial of service (DDoS) attack (active attack) .
Novel approaches to mitigate cybersecurity threats are being developed considering the cost (both financial and physical) of dealing with cybersecurity attacks in the power system domain. Presekal et al., develop an attack graph model for cyber-physical power systems using hybrid deep learning to mitigate cyber threats on a digital substation level.
A similar approach for a general cyber-physical power system combining graph CNN for network topology and LSTM for temporal learning can be found in .
Additionally, Electrical power grids are vulnerable to cyber-attacks, existing attack detection methods are limited so to tackle Graph Convolutional Long Short-Term Memory (GC-LSTM) with a deep convolution network has been proposed to further improve time series classification and analysis with respect to anomaly detection and attack graph models .