With the proliferation of embedded technologies and wireless capabilities, today's vehicles are no longer isolated mechanical machines. They become part of a hyper-connected system -Intelligent Transportation Systems (ITS)- that has the potential to support multiple levels of autonomy and intelligence improving considerably the safety, efficiency, and sustainability of transportation networks. However, this raises new security issues that make the whole system prone to cybersecurity attacks that threaten both the safety and privacy of all road-users. This article gives a short background tutorial on the main security issues and the different attacks that hinder Intelligent Transport Systems. To enable secure and safe ITS applications, this article provides a comprehensive analysis of existing solutions and highlights their strengths and limitations. Finally, this survey presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and car manufactures to improve the security of ITS.

With the development of modern Intelligent Transportation System (ITS), reliable and efficient transportation information sharing becomes more and more important. Although there are promising wireless communication schemes such as Vehicle-to-Everything (V2X) communication standards, information sharing in ITS still faces challenges such as the V2X communication overload when a large number of vehicles suddenly appeared in one area. This flash crowd situation is mainly due to the uncertainty of traffic especially in the urban areas during traffic rush hours and will significantly increase the V2X communication latency. In order to solve such flash crowd issues, we propose a novel system that can accurately predict the traffic flow and density in the urban area that can be used to avoid the V2X communication flash crowd situation. By combining the existing grid-based and graph-based traffic flow prediction methods, we use a Topological Graph Convolutional Network (ToGCN) followed with a Sequence-to-sequence (Seq2Seq) framework to predict future traffic flow and density with temporal correlations. The experimentation on a real-world taxi trajectory traffic data set is performed and the evaluation results prove the effectiveness of our method.

Autonomous Driving Systems (ADS) represent a revolutionary advancement in transportation and offer unprecedented safety and convenience. Real-world physical attacks are emphasized because Autonomous Driving Systems (ADS) depend heavily on sensors and perception modules to detect and interpret their surroundings, making security a critical concern. Defenders usually have the upper hand in the digital sphere while they are challenged in the physical world because attackers have greater flexibility for covert operations. A comprehensive analysis is essential for understanding attack trends, evolution, and defense directions. This paper provides a survey of stateof-the-art physical attacks that threaten ADS perception. A novel multi-label classification method is introduced to categorize these attacks along four main dimensions. Visualization and analysis of the classification enhance the understanding of these multidimensional threats. Five research directions for future exploration are also proposed.

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning–blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.

The rapid growth of the telecommunication landscape leads to a rapid rise of frauds in such networks. In this article, Wangiri fraud in which users are deceived by being charged for services without their knowledge during a call is tackled. In fact, Wangiri fraud has significant negative financial and reputation consequences for the mobile service providers and also has a bad psychological impact on the victims. In order to identify this fraudulent behavior, three Wangiri fraud patterns are defined by analyzing call records of over a year. Then, the security and performance of unsupervised and supervised machine learning (ML) methods in detecting one Wangiri pattern are evaluated using a large real-world Call Detail Records (CDRs) data set. In the context of Wangiri fraud detection, classification algorithms outperformed the others based on the chosen security and performance metrics. Finally, the performance evaluation of these algorithms is extended in detecting the other two real-world Wangiri fraud patterns. This article provides a detailed definition of the Wangiri fraud patterns and outlines the implementation and evaluation of ML algorithms in the context of detecting Wangiri fraud. The security analysis and experimental results demonstrate that depending on fraud patterns the best ML algorithm to detect Wangiri fraud may also vary.