Smart Grid (SG) networks, as a part of critical national infrastructure, are vulnerable to sophisticated cyber-physical attacks. Specifically, a coordinated false data injection attack aiming to generate fake transient measurements in the SG's Automatic Generation Control (AGC), can cause unwarranted actions and blackouts in the worst scenario. Unlike other works that overlook contextual correlations, this work utilizes contextual prior information and a temporal model to detect cyber-attacks. Specifically, we depart from the traditional deep learning anomaly detection, driven by black-box detection; instead, we envision an approach based on physics-informed hybrid deep learning detection. Our approach utilizes the combination of process control-based variational autoencoder, prior knowledge of physics, and long short-term memory for a false data injection attack detection. To the best of our knowledge, our method is the first contextual-based anomaly detection that incorporates process control-based prior information in the smart grid. The proposed approach is evaluated on the modified high-class PowerWorld simulated dataset based on the IEEE 37-bus model. Our experiments observe the lowest reconstruction error and offer 96.9% accuracy, demonstrating superiority over other baselines.

Automatic Generation Control (AGC) is used in smart grid systems to maintain the grid's frequency to a nominal value. Cyber-attacks such as time delay and false data injection on the tie-line power flow, frequency measurements, and Area Control Error (ACE) control signals can cause frequency excursion that can trigger load shedding, generators' damage, and blackouts. Therefore, resilience and detection of attacks are of paramount importance in terms of the reliable operation of the grid. In contrast with the previous works that overlook ACE resiliency, this paper proposes an approach for cyber-attack detection and resiliency in the overall AGC process. We propose a state estimation algorithm approach for the AGC system by utilizing prior information based on Gaussian process regression, a non-parametric, Bayesian approach to regression. We evaluate our approach using the PowerWorld simulator based on the three-area New England IEEE 39-bus model. Moreover, we utilize the modified version of the New England ISO load data for the three-area power system to create a more realistic dataset. Our results clearly show that our resilient control system approach can mitigate the system using predictive control and detect the attack with a 100 percent detection rate in a shorter period using prior auxiliary information.

Successful deployment of Long-Range Wide Area Network (LoRaWAN) technology in several Industrial Internet of Things (IIoT) scenarios, such as Outage Management System (OMS) in smart metering, rely on low energy consumption of the end device. In this work, we conducted an experiment to demonstrate an on-off Denial-of-Service (DoS) attack to analyze the impact on the energy consumption of the LoRaWAN end device. We implemented the attack that manipulates the end device to remain in packet retransmission mode for several seconds. The conducted experiments show that the configurable parameters of LoRaWAN that are required for applications, like OMS, are susceptible to energy consumption attacks. In summary, our results show that when an on-off DoS attack is performed, the end device utilizing the Spreading Factor (SF) 12 consumes 92 times more energy due to packet retransmissions as compared to the end node using SF 7 under no attack.