In millimeter wave wireless networks with directional antennas, the design of efficient multicast communications is one of the most challenging issues. In this letter, we propose an incremental multicast grouping (IMG) scheme where adaptive beamwidths are generated depending on the locations of multicast devices to maximize the sum rate of devices. We develop a simulator based on the IEEE 802.11ad MAC protocol. Simulation results demonstrate that the IMG scheme can improve the overall throughput by 28% to 79% compared with the conventional multicast schemes.

The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms.

The communication and connectivity functions of vehicles increase their vulnerability to hackers. The unintended failure and malfunction of in-vehicle systems caused by external factors threaten the security and safety of passengers. As the controller area network alone cannot protect vehicles from external attacks, techniques to analyze and detect external attacks are required. Therefore, we propose a multi-labeled hierarchical classification (MLHC) intrusion detection model that analyzes and detects external attacks caused by message injection. This model quickly determines the occurrence of attacks and classifies the attack using only existing classified attack data. We evaluated the performance of the model by analyzing its learning space. We further verified the model by comparing its accuracy, F1 score and data learning and evaluation times with the two layers multi-class detection (TLMD) and single-layer multi-class classification (SLMC) models. The simulation results show that the MLHC model has the highest F1 score of 0.9995 and is 87.30% and 99.92% faster than the SLMC and TLMD models in terms of detection time, respectively. Consequently, the proposed model can classify both the type and existence or absence of attacks with high accuracy and can be used in interior communication environments of high-speed vehicles with a high throughput.

As more vehicles are being connected to the Internet and equipped with autonomous driving features, more robust safety and security measures are required for connected and autonomous vehicles (CAVs). Therefore, threat analysis and risk assessment are essential to prepare against cybersecurity risks for CAVs. Although prior studies have measured the possibility of attack and damage from attack as risk assessment indices, they have not analyzed the expanding attack surface or risk assessment indices that rely upon real-time resilience. This study proposes the PIER method to evaluate the cybersecurity risks of CAVs. We implemented cyber resilience for CAVs by presenting new criteria, such as exposure and recovery, in addition to probability and impact, as indices for the threat analysis and risk assessment of vehicles. To verify its effectiveness, the PIER method was evaluated with respect to software update over-the-air and collision avoidance features. Furthermore, we found that implementing security requirements that mitigate serious risks successfully diminishes the risk indices. Using the risk assessment matrix, the PIER method can shorten the risk determination time through high-risk coverage and a simple process.