University Events

Description: Topic: VRAD Gen: Dataset Generator for Machine Learning Detection of Vehicle-Roadside Attacks

Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A

Zoom Conference Link: https://umassd.zoom.us/j/93281343753
Meeting ID: 932 8134 3753 Passcode: 518247

Abstract:
Autonomous vehicles are gaining popularity rapidly for transportation safety and efficiency. However, to achieve fully automated driving without human intervention, cars need to be aware of traffic and environmental dynamics. One key component of their awareness is through communication. Connected and Automated Vehicles (CAV) leverage both automation and communication technologies to obtain information with nearby devices for decision making. Vehicular Ad-Hoc NETworks (VANETs) provide an architecture to support CAV with cars’ On-Board Units (OBUs) to communicate with each other and other devices like Roadside Units (RSUs). This communication can involve sharing information about traffic conditions, road hazards, and other data. Security of RSUs is crucial to ensure reliability and trustworthiness of its communication. Some security concerns with RSUs are authentication of RSUs, data integrity, etc. Addressing these concerns will require multiple types of mechanisms such as encryption and intrusion detection systems (IDS) to prevent misbehaving RSUs. With any type of communication, security of information is always at the forefront. Conventional security techniques have been tested, such as password protection and biometric security, but they do not meet the needs of the high dynamics of VANET. Machine learning and Deep learning have been proposed as viable options. Machine and Deep learning can learn without human intervention, which is highly desirable to CAV. Since CAV is still in its infancy, there is little data to train and test Machine and Deep learning algorithms. Some attempts to synthesize attack datasets but only on vehicle-to-vehicle (V2V) communication. To the best of our knowledge, there is no data generator for vehicle-to-infrastructure (V2I). This research fills the gap by providing a dataset generator to inject misbehaving RSUs. Using simulation, data is generated to be able train and test several algorithms. Our work reveals traditional machine learning algorithms are not sufficient to solve VANET security problems. The deep learning algorithm shows promise, but more analysis will be needed to be suitable for VANET security.

Co-Advisor(s):
Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth;
Dr. Prinkle Sharma, Assistant Professor, Department of Information Security and Digital Forensics, University at Albany-SUNY, Albany, NY

Committee Members:
Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth;
Dr. Honggang Wang, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.

*For further information, please contact Dr. Hong Liu via email at hliu@umassd.edu