Research Projects
These are the current research projects in my research lab.
Research Interests: Vehicular Networks, Cybersecurity, Privacy, Blockchain, Connected and Autonomous Vehicles, Electric Vehicle Charging.
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Elements of network for security assessment:
Vehicle-to-Vehicle Communication
Vehicle-to-Infrastructure Communication
Vehicle-to-Grid Communication
Over-the-Air Updates
In-Vehicle Networks
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Roadside Unit Misbehaviour Detection in VANET
As smart transportation systems evolve, the integrity and reliability of communications within VANETs become crucial. Our project focuses on identifying and mitigating misbehaviour by roadside units (RSUs), which are critical components in ensuring safe and efficient traffic management. Through advanced detection algorithms and robust security protocols, we aim to enhance the trustworthiness of VANETs, prevent malicious and disruptive activities, and ensure a safer driving environment.
Secure Electric Vehicle Charging Communication
As electric vehicles gain popularity, ensuring secure and efficient communication between charging stations and EVs is paramount. Our research focuses on developing and implementing advanced cybersecurity measures to protect data integrity and prevent unauthorized access. By addressing potential vulnerabilities and offering innovative solutions, we aim to create a safer, more reliable EV charging infrastructure that supports the expanding demand for sustainable transportation.
Misbehaviour Detection in Vehicle-to-Vehicle Communication
In the realm of intelligent transportation systems, the reliability and security of V2V communication are paramount. Our project is dedicated to identifying and addressing misbehaviour among vehicles, ensuring that data exchanged between them remains trustworthy and accurate. By developing advanced detection algorithms and implementing robust security measures, we aim to prevent malicious activities, enhance traffic safety, and foster a more reliable V2V communication network.
In-Vehicle Intrusion Detection System
In-vehicle networks (IVNs) are crucial for the modern automotive industry, enabling communication between various Electronic Control Units (ECUs) that manage different vehicle functions. These networks facilitate the exchange of data among subsystems, enhancing vehicle performance, safety, and convenience. The Controller Area Network (CAN) is one of the most widely used IVN protocols, renowned for its robustness and efficiency in real-time communication. The objective of this research is to develop a Hybrid Framework for ML-based IDS to secure the in-vehicular network, such as the CAN network.
