Sixth-generation (6G) communication is emerging as seamless and massive connecting of almost everything. Therefore, vehicles, being extensively linked with human mobility, require a technological pace for compatibility with the 6G era. 6G will also potentially revolutionize the Vehicle-to-Everything (V2X) communication. However, this modernization will surface several security challenges in the complex heterogeneous architecture of V2X communication in 6G. Similarly, the expansion of V2X also introduces unconventional security risks and vulnerabilities. This paper aims to provide an overview of the security challenges and solutions for V2X communication in the upcoming 6G era to visualize the future of this research domain. This paper discusses the architecture and standards utilized in 6G enabled V2X communications and provisions a comprehensive analysis of V2X security in Confidentiality, Integrity, Availability, Authentication and Access Control (CIA ³ ) domains. Thereby, we analyze the impact of the emerging technological concepts of Blockchain and Federated Learning (FL) in 6G enabled V2X communication. Thereby, we suggest a Blockchain-enabled FL based generic security architecture for V2X communication in 6G networks. At the conclusive end, the review highlights key lessons learned and the future research directions in the domain of security of V2X communications in the 6G including; Privacy in 3D Fog Computing, Privacy in Augmented Reality, C. Secure Software Defined Networking (SDN), Physical Layer Security In THz Spectrum, SUMO (Simulation of Urban MObility) and Intrusion Detection using AI.

According to the Fire-eye’s M-Trends Annual Threat Report 2022, there are many advanced persistent threat (APT) attacks that are currently in use, and such continuous and specialized APT attacks cause serious damages attacks. As APT attacks continue to be active, there is a need for countermeasures to detect new and existing malicious codes. An APT attack is a type of intelligent attack that analyzes the target and exploits its vulnerabilities. It attempts to achieve a specific purpose, and is persistent in continuously attacking and threatening the system. With this background, this paper analyzes attack scenarios based on attack cases by malicious code, and surveys and analyzes attack techniques used in attack cases. Based on the results of the analysis, we classify and analyze malicious code detection techniques into security management systems, pattern-based detection, heuristic-based detection, reputation-based detection, behavior-based detection, virtualization-based detection, anomaly detection, data analysis-based detection (big data-based, machine learning-based), and others. This paper is expected to serve as a useful reference for detecting and preventing malicious codes. Specifically, this article is a surveyed review article.

With the advent of 5G technology, the enhanced Mobile Broadband technology is translating 5G-based Internet of Things (IoT) such as smart home/building into reality. With such advances, security must mitigate greater risks associated with faster and more accessible technology. The 5G-based IoT security analysis is crucial to IoT Technology, which will eventually expand extensively into massive machine-type communications and Ultra-Reliable Low Latency Communications. This paper analyses the countermeasures and verification methods of eavesdropping vulnerabilities within IoT devices that use the current 5G Non-Standalone (NSA) network system. The network hierarchical structure of 5G-based IoT was evaluated for vulnerability analysis, performed separately for 5G Access Stratum (AS), Non-Access Stratum (NAS), and Internet Protocol (IP) Multimedia Subsystem (IMS). AS keystream reuse, NAS null-ciphering, and IMS IPsec off vulnerabilities were tested on mobile carrier networks to validate it on the 5G NSA network as well. A countermeasure against each vulnerability was presented, and our Intrusion Detection System based on these countermeasures successfully detected the presented controlled attacks.

We propose a lightweight specification-based misbehavior detection management technique to efficiently and effectively detect misbehavior of an IoT device embedded in a medical cyber physical system through automatic model checking and formal verification. We verify our specification-based misbehavior detection technique with a patient-controlled analgesia (PCA) device embedded in a medical health monitoring system. Through extensive ns3 simulation, we verify its superior performance over popular machine learning anomaly detection methods based on support vector machine (SVM) and k-nearest neighbors (KNN) techniques in both effectiveness and efficiency performance metrics.

The Controller Area Network (CAN) is a widely used communication protocol in automobiles, but it is vulnerable to various types of attacks. To address this issue, researchers have been exploring the use of intrusion detection systems (IDS) for the CAN bus. Deep learning and machine learning have been proven to be powerful tools for detecting intrusions accurately and quickly. However, deep learning models require large amounts of data to achieve optimal performance, which can be challenging in the case of a CAN bus IDS. To overcome this challenge, we propose a novel machine learning-based IDS called CANPerFL that uses a personalized federated learning scheme to aggregate datasets from different car models. By building a universal model trained on a small amount of data from each manufacturer, we can provide global knowledge that can be transferred to improve the performance of each participant. To demonstrate the efficiency of the proposed model, we collected a real CAN dataset consisting of three different car models: KIA, BMW, and Tesla. The experimental results show that the proposed model increases F1 scores by 4% overall, compared to baselines. Moreover, the proposed system provides significant advantages when the local dataset of each participant is relatively small. According to our experiments, the proposed models can achieve F1 scores of more than 90% with at least 30k training samples on each client. Finally, we show empirically that each participant takes benefits from joining the CANPerFL system.