The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security.

The extent of renewable power generation usage has increased in recent times, and it will become inevitable soon to generate clean and green power. Microgrid units use renewable energy for power generation. The inclusion of Distributed energy resources (DER) will lead to intermittent power generation because of the continuously changing weather and seasonal conditions. The electricity consumption of the end-user also changes according to the time and the seasons. The source forecasting and the load forecasting becomes very important to schedule the energy storage device operations. In this paper, we use Solar energy as the source,solar irradiance changes with respect to place and time. In this article, Solar forecasting is performed for one month. If in case there are occurrences of an event like days of autonomy, effective load management can be performed provided a prior possession of knowledge about the source availability. We have different Artificial intelligence techniques like Fuzzy Logic, Machine learning (ML), and Deep learning (DL) mechanisms for source forecasting. When the microgrid is set up at a particular place, the irradiance, data and other parameters are considered for building the dataset. This article consists of a brief introduction about the microgrid and different source forecasting techniques. Machine learning algorithms and Deep learning algorithms are discussed and the efficiency of the various algorithms are compared using the Root Mean Square Error (RMSE) values.

Power electronic converters have become an integral part of the direct current microgrid (DCMG) systems. The efficient control of these converters will decide the performance of the DC microgrids. With the evolution of cyber physical systems, all these power converters are integrated into the communication networks to achieve intelligent and smart control. Data in the communication networks are highly vulnerable towards cyber-attacks, leaving it unaddressed will lead to substantial economic losses and disasters. This article proposes a standalone implementation of multi-deep neural network (MDNN) based DC-DC converter and its application to detect false data injection (FDI) attacks at the sensor level. A MDNN is developed with a combination of deep neural network (DNN) and error detection network (EDN). DNN is used as the controller to achieve closed loop operation of DC-DC converter and EDN is used to detect and mitigate the FDI attack. Initially, proposed scheme is executed in MATLAB simulink platform with various disturbances at controller level and several attack scenarios are verified with its experimental results.

It is hard to monitor human activities in various contexts like security surveillance, healthcare, and human-computer interaction. Human Activity Recognition is the process of predicting what a person is doing based on the traces of their movement. We propose using deep recurrent neural networks (DRNNs) for building recognition models capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM), DRNNs and evaluate their effectiveness on various benchmark datasets. Long shortterm memory (LSTM) is an artificial deep recurrent neural network (DRNN) architecture used in deep learning, especially for time series prediction. It can process single data points (such as images) and entire data sequences (such as speech or video). LSTM networks are well-suited for classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between essential events in a time series in real-time. We proposed Human Activity Recognition (HAR) using a smartphone dataset and LSTM. Compared to a classical approach, using Deep Recurrent Neural Networks (DRNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network, which acts as a black box, modeling the problem correctly. This means that the neural networks are almost always able to identify the movement type correctly. We used jupyter notebook with python 3.7+.

This article introduces a novel approach to enhance the cybersecurity of cyber-physical systems (CPS) by integrating a Hybrid Physics Informed Neural Network (HPINN) into the control framework of a DC-DC converter. The proposed system leverages the strengths of both physics-based modeling and datadriven techniques to detect and mitigate false data injection attacks (FDIA) affecting the converter's performance. This article proposes a novel HPINN based methodology for FDIA attack detection and mitigation. The proposed methodology includes the design of PINN based attack detection module and PINN prediction module, the combination of these two modules ensures robust control during system uncertainties and reliable operation during FDIA scenarios. PINN detection module is a combination of state estimator and NN classifier. The article presents the architecture of the hybrid system and details its training process, emphasizing the integration of physical principles to enhance the model's interpretability and generalization. Simulation is performed in MATLAB Simulink 2023b; Simulation results demonstrate the effectiveness of the proposed approach in detecting and mitigating FDIA, including those attempting to manipulate the converter's output or compromise its stability. Later, real-time implementation of the proposed method is performed by developing experimental hardware and deploying the HPINN model into the Atmel SAM3X8E ARM Cortex-M3 microcontroller. The proposed scheme is analyzed for various scenarios like load changes, constant FDIA, and time-varying FDIA and its results are explored.