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Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks. DL has emerged as a promising ML technique that adapts multiple layers of representations or features of the data and yields state-of-the-art extrapolation results. In recent years, along with the success of ML and DL in many other application domains, both ML and DL are also popularly used in SM analysis. This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis. Finally, this review serves the purpose of offering suggestions, ideas, and proposals, along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed. [ABSTRACT FROM AUTHOR]

Urban air mobility, also known as UAM, is currently being researched in a variety of metropolitan regions throughout the world as a potential new mode of transport for travelling shorter distances inside a territory. In this article, we investigate whether or not the market can back the necessary financial commitments to deploy UAM. A challenge in defining and addressing a critical phase of such guidance is called a demand forecast problem. To achieve this goal, a deep learning model for forecasting temporal data is proposed. This model is used to find and study the scientific issues involved. A benchmark dataset of 150,000 records was used for this purpose. Our experiments used different state-of-the-art DL models: LSTM, GRU, and Transformer for UAM demand prediction. The transformer showed a high performance with an RMSE of 0.64, allowing decision-makers to analyze the feasibility and viability of their investments. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework's network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%. [ABSTRACT FROM AUTHOR]