Security for Complex Cyber-Physical and Industrial Control Systems: Current Trends, Limitations, and Challenges

Today’s society relies upon the smooth and secure functioning of the mission-critical infrastructures and their services. Much of this critical infrastructure relies on the complex cyber-physical systems and the industrial control systems. In recent years, securing these two types of systems has been a top priority due to a significant increase in number of attacks. Most of these systems are often several decades old, and they were developed without significant consideration of the security requirements. As such, there is an urgent need to protect these cyber-physical and industrial systems from external vulnerabilities. In this paper, we present a survey of the cyber-physical and industrial control systems, and explore the possibility and necessity for security of such systems. We discuss the various types of cyber-physical and industrial control systems currently being used, assess the vulnerabilities of such systems, discuss the literature on the cyber-physical and industrial control systems, and examine some works that propose security standards and models for these types of systems

Inductive Transfer and Deep Neural Network Learning-Based Cross-Model Method for Short-Term Load Forecasting in Smarts Grids

In a real-world scenario of load forecasting, it is crucial to determine the energy consumption in electrical networks. The energy consumption data exhibit high variability between historical data and newly arriving data streams. To keep the forecasting models updated with the current trends, it is important to fine-tune the models in a timely manner. This article proposes a reliable inductive transfer learning (ITL) method, to use the knowledge from existing deep learning (DL) load forecasting models, to innovatively develop highly accurate ITL models at a large number of other distribution nodes reducing model training time. The outlier-insensitive clustering-based technique is adopted to group similar distribution nodes into clusters. ITL is considered in the setting of homogeneous inductive transfer. To solve overfitting that exists with ITL, a novel weight regularized optimization approach is implemented. The proposed novel cross-model methodology is evaluated on a real-world case study of 1000 distribution nodes of an electrical grid for one-day ahead hourly forecasting. Experimental results demonstrate that overfitting and negative learning in ITL can be avoided by the dissociated weight regularization (DWR) optimizer and that the proposed methodology delivers a reduction in training time by almost 85.6% and has no noticeable accuracy losses.Peer reviewe

Real-Time Big Data Analytics with Computational Intelligence Approaches for Energy Load Forecasting

In a real-time scenario of load forecasting, it is crucial to determine the future electric energy consumption in power distribution electrical networks. The electric energy forecasting models need to be updated with real-time trends of energy consumption as the analyzed energy consumption data exhibits high variability between historical and current data. This work proposes a multi-stage supercomputing-based big data analytics service for parallel and real-time load forecasting. Moreover, theoretical and experimental perspectives are proposed for multi-core parallel short-term load forecasting. Additionally, the knowledge from existing load forecasting based on deep learning models is used to innovatively develop highly accurate transfer learning models at different distribution nodes. Transfer learning models present practical applicability and productive possibilities in cases when sufficiently large data is not available. A novel approach based on deep neural network models is employed for load forecasting. Firstly, the electrical distribution nodes are grouped into different clusters with an aim to decrease the number of deep learning models to be trained. Secondly, network architecture information, weights, and biases are transferred from the first developed clustered model to subsequent models with an aim to reduce the training time of a large number of clustered models. And incremental learning is employed to incorporate newer data points for real-time processing and improving the forecasting accuracy of the clustered models on individual distribution points. Furthermore, parallel pool-based processing is employed to make efficient utilization of computing cores and to reduce the model development time further. The proposed big data real-time analytics methodology is evaluated on real-world energy consumption data collected from 105,148 Spanish electrical distribution transformers. The proposed methodology aims to reduce the number of trained models, training time, and execution time while still maintaining high prediction accuracy

Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption

Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh

Deep Learning-based Face Mask Usage Detection on Low Compute Resource Devices

The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily lives of people around the globe. As a way to curb the spreading of the disease, wearing face masks has become mandatory in the majority of public places. To solve the necessity of face mask detection in such situations, there have been only a handful of research endeavors up to this date. Computer vision has advanced multi-fold with the advent of AlexNet architecture. With a motivation to go deeper with the neural network architecture, the concept of Depthwise Separable Convolutions and projection layer was developed in MobileNetV1. In this work, a novel lightweight deep learning model based on Single Shot Detector (SSD) MobileNetV2 architecture is proposed for face mask detection using images and video streams of crowds aiming its utilization on low compute re-source environment. An open benchmark face mask dataset, with 4095 images including masked and no mask images, is utilized to train the model for detection. The model is initialized using transfer learning with the freezing of base layers. The proposed methodology can efficiently aid in tracking and enforcing social distancing rules in crowded places with the use of surveillance cameras. On the different benchmarks that we have tested, the model proved to be highly successful and has achieved an accuracy rate of 99.39% and an F1 score of 0.995

Towards heat tolerant metagenome functional prediction, coral microbial community composition, and enrichment analysis

Coral reefs represent one of the most biodiverse marine ecosystems on our planet. These consist of colonies of very small sea animals belonging to the phylum named Cnidaria, and of more complex, yet not so well-known microbial communities. Despite the fact that they occupy only a tiny portion of the oceans\u27 surface, coral reefs are swarming with life, providing food and shelter to a wide number of marine species, ranging from mollusks to numerous fish species. There is a number of factors that can affect their sustainability and likelihood of developing diseases, including increased seawater temperature, acidity, salinity, and human impact. It is crucial to study the relationship between corals and microbial communities linked to them. This work analyzes the overall microbial community composition of the different coral species found in the Australian waters and identifies the most abundant Operational Taxonomic Units (OTUs) on different taxonomic levels. Additionally, heat specific coral core microbiome found across at least 20% of the investigated coral host species was identified and thoroughly analyzed. Lastly, metagenome functional prediction was carried out and the most abundant heat tolerance related genes were highlighted