Gary Stevens

Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) approaches. To accurately detect various types of attacks in IoV networks, we propose a novel ensemble IDS framework named Leader Class and Confidence Decision Ensemble (LCCDE). It is constructed by determining the best-performing ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost) for every class or type of attack. The class leader models with their prediction confidence values are then utilized to make accurate decisions regarding the detection of various types of cyber-attacks. Experiments on two public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate the effectiveness of the proposed LCCDE for intrusion detection on both intra-vehicle and external networks.

A comprehensive knowledge of the available grounding strategies and their effects is essential for design, operation, and protection of the dc microgrid. This paper investigates and compares different dc microgrid grounding strategies that involve the choice of grounding configurations and grounding devices. The impacts of different grounding strategies on line-to-ground (LG) fault detection and protection, transient LG fault current magnitude, leakage current level, common mode voltage, personnel/equipment safety, system reliability, service continuity, and insulation requirements are thoroughly investigated. LG fault response of a low-voltage dc microgrid with different grounding devices is studied by detailed simulation of a realistic study system modeled in the PSCAD software. The study results indicate that: 1) the ungrounded, bipolar solidly grounded, unipolar parallel resistance grounded, and bipolar resistance grounded dc microgrids enable LG fault ride through, due to the low fault current; 2) the unipolar solidly grounded, bipolar solidly grounded, diode-grounded, and thyristor-grounded dc microgrids experience significantly large transient discharge currents under LG faults; and 3) the unipolar solidly grounded, diode-grounded, thyristor-grounded, and unipolar low-resistance-grounded dc microgrids provide considerable steady-state LG fault current which necessitates fast detection and clearance of faults.