[Eecs_phd] EECS Research Seminar Talk With EE Interview Candidate Jiaqi Huang– Secure V2X Communications: Lightweight Authentication and Machine Learning-based Intrusion Detection

[Hunter, Tiffany]

Title:
Secure V2X Communications: Lightweight Authentication and Machine Learning-based Intrusion Detection

Abstract:
Wireless communication networks, bolstered by the integration of artificial intelligence and cloud/edge computing, have revolutionized numerous applications and cyber-physical systems, including Internet of Things, Smart Power Grids, Intelligent Transportation Systems, and healthcare systems. However, the ubiquitous nature of wireless connectivity also brings forth significant security challenges. Safeguarding network devices and protecting user interests demand meticulous attention to security concerns. In this presentation, Dr. Huang will delve into the security landscape of vehicular networks. He will commence by unveiling his innovative lightweight security solution designed to fortify vehicular communications against external threats. Subsequently, he will introduce machine-learning-based intrusion detection systems aimed at countering internal threats. Lastly, Dr. Huang will outline his prospective research endeavors focusing on security and machine learning within computer and communication networks.

Bio:

[A person wearing glasses and a suit  Description automatically generated with medium confidence]Dr. Jiaqi Huang is an assistant professor in the school of Computer Science and Cybersecurity at the University of Central Missouri. He received his Ph.D. in Computer Engineering from the University of Nebraska-Lincoln in 2020 and his BS degree from the University of Electronic Science and Technology of China in 2016. Dr. Huang's research focuses on cybersecurity, machine learning, wireless communication systems and networks. His work has been published in leading international journals and technical conferences in his field. Additionally, Dr. Huang is a certified security professional, holding a GREM (Global Information Assurance Certification-Reverse Engineering of Malware) from the SANS Institute.






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