Smart grids rely on SCADA (Supervisory Control and Data Acquisition) systems to monitor and control complex electrical networks in order to provide reliable energy to homes and industries. However, the increased inter-connectivity and remote accessibility of SCADA systems expose them to cyber attacks. As a consequence, developing effective security mechanisms is a priority in order to protect the network from internal and external attacks. We propose an integrated framework for an Intrusion Detection System (IDS) for smart grids which combines feature engineering-based preprocessing with machine learning classifiers. Whilst most of the machine learning techniques finetune the hyper-parameters to improve the detection rate, our approach focuses on selecting the most promising features of the dataset using Gradient Boosting Feature Selection (GBFS) before applying the classification algorithm, a combination which improves not only the detection rate but also the execution speed. GBFS uses the Weighted Feature Importance (WFI) extraction technique to reduce the complexity of classifiers. We implement and evaluate various decision-tree based machine learning techniques after obtaining the most promising features of the power grid dataset through a GBFS module, and show that this approach optimizes the False Positive Rate (FPR) and the execution time.

Supervisory Control and Data Acquisition (SCADA) systems are used for monitoring industrial devices. However, their security faces the threat of being compromised due to the increasing use of open access networks. The primary objective of this survey paper is to provide a comparative study of the on-going security research in SCADA systems. The paper provides a classification of attacks based on security requirements and network protocol layers. To secure the communication between nodes of SCADA networks, various security standards have been developed by different organizations. We conduct a study of the security standards developed for SCADA networks along with their vulnerabilities. Researchers have proposed various security schemes to overcome the weaknesses of SCADA standards. The paper organizes security schemes based on current standards, detection, and prevention of attacks. It also addresses the future challenges that SCADA networks may face, in particular, from quantum attacks. Furthermore, it outlines directions for further research in the field.

We propose an integrated framework for an intrusion detection system for SCADA (Supervisory Control and Data Acquisition)-based power grids. Our scheme combines RFE-XGBoost (Recursive Feature Elimination-eXtreme Gradient Boosting) based feature selection with a majority vote ensemble method. RFE selects features recursively based on Weighted Feature Importance (WFI) scores during the training process, while the majority vote ensemble method predicts the output label based on a total of nine heterogeneous classifiers - three bagging ensembles, namely, Random Forest (RF), Extra Tree (ET), and Decision Tree (DT), three boosting ensembles, namely, XGBoost (XGB), Gradient Boosting (GB), and AdaBoost-Decision Tree (AdB-DT) along with artificial neural network (ANN), Naive Bayes (NB), and k-nearest neighbors (KNN). This leads to a more accurate solution as a result of the combination of the most useful features and prediction from multiple heterogeneous classifiers. Experimental results show that our approach increases the accuracy, precision, recall, F1 score, and decreases the miss rate as compared to previous approaches. The model is also evaluated for four different class categories, namely binary, three-class, seven class and multi-class, using Precision Recall (PR) and Receiver Operating Characteristic (ROC) plot. In addition, an end-to-end IDS framework is proposed for efficient and accurate detection of intrusions.

The Internet of Things (IoT) encompasses a network of physical objects embedded with sensors, software, and data processing technologies that can establish connections and exchange data with other devices and systems via the Internet. IoT devices are incorporated into various products, ranging from ordinary household items to complex industrial appliances. Despite the increasing demand for IoT, security concerns have impeded its development. This article systematically reviews IoT security research, focusing on vulnerabilities, challenges, technologies, and future directions. It surveys 171 recent publications in the field, providing a comprehensive discussion on the development status, challenges, and solutions in IoT. The article outlines IoT architecture patterns and typical features, evaluates existing limitations, and explores strategies for enhancing IoT security. Additionally, the article delves into known IoT attacks and discusses the security countermeasures and mechanisms to address these challenges. It explores the functional requirements of IoT security and explores related technologies and standards. Finally, the article discusses potential future research directions in IoT security.

Internet of Things (IoT) is an important technology employed in a variety of different applications, such as transportation, healthcare, and manufacturing. In recent years, the number of IoT devices deployed globally has been increasing at a rapid pace and is estimated to reach 20 billion by the end of 2025. In modern industry, IoT plays a pivotal role by monitoring the condition of industrial machines and, consequently, improving the efficiency of industrial processes. To optimize the efficiency of industrial IoT applications, various Artificial Intelligence (AI) techniques have been adopted, leading to a new computing paradigm, namely, Industrial Artificial Intelligence of Things (i.e. Industrial AIoT). In this paper, we describe the challenges to tackle and the opportunities to explore in Industrial AIoT. Specifically, we first review the use of state-of-the-art AI methods in Industrial AIoT applications, with a focus on Deep Learning (DL) and Machine Learning (ML) techniques. Thereafter, we present a series of important applications of Industrial AIoT. The key challenges associated with the implementation of Industrial AIoT applications are also discussed. In addition, the societal and economic impacts of Industrial AIoT are briefly described. Finally, we outline the future research directions in Industrial AIoT, which should be further investigated to fully utilize the potential of this innovative technology.