EXCELLENCE in RESEARCH: SECURING MACHINE LEARNING AGAINST ADVERSARIAL ATTACKS FOR CONNECTED AND AUTONOMOUS VEHICLES

Benedict College, Columbia SC

Investigators

Abstract

This research is motivated by the need to boost U.S. competitiveness and increase the number of young people with an in-depth understanding of the safety, security, and dependability of intelligent systems by accelerating the adoption of threat identification and attack-resistant control countermeasures. Future cyberattacks on connected and automated vehicles will necessitate the study and development of novel countermeasures to increase market acceptance of these vehicle technologies, which could improve traffic conditions, vehicle and personal safety, and energy efficiency. This award will contribute to the intellectual development of underrepresented undergraduate and graduate students in modeling, Artificial Intelligence, and communication to address cybersecurity issues in connected autonomous vehicles. The prime objective of this research is to create a defense technique that will enable Connected Autonomous Vehicles to be more resistant to adversarial attacks and hence capable of meeting more stringent safety and performance requirements. Another key focus is to involve teams of undergraduate and graduate students in creative inquiry and design projects based on hands-on demo platforms. This research focuses especially on enhancing the resilience of Connected Autonomous Vehicles against the possibility of adversarial attacks aimed at affecting the performance of the perception module, thereby improving vehicle reliability and functional safety beyond currently adopted practices. In addition, the award has larger theoretical implications in the fields of security, Machine Learning, filtering, and optimization, ultimately expediting the deployment of connected autonomous vehicles. Recent advancements in connected and autonomous vehicles reveal that several companies are investing substantially in the development of perception modules based on machine learning algorithms. However, these machine learning algorithms are vulnerable to adversarial attacks designed to mislead the input of the deep neural network to induce a misclassification, which may undermine vehicle decision-making and, therefore, functional safety. Through wireless Ethernet connectivity, attackers may compromise the in-vehicle computer platform and obtain access to the sensor data stored in memory. Before the perception module, adversarial inputs may be introduced to supplant the original normal inputs and destabilize vehicle operations. The overall framework comprises modeling potential adversarial threats impacting the perception and fusion process and designing both reactive and proactive countermeasures for the secure and reliable functioning of the system. This technique is modular and can be deployed to a range of Deep Neural Network applications such as robotics, biometric identification, and speech recognition. Incorporating robustness measures during the training phase will yield a more resilient Deep Neural Network. Moreover, filtering at several stages of the perceptron process can be used to develop a system that can innately tolerate a greater spectrum of attacks. The project tasks aim at conducting fundamental research on a plan that includes adversarial attack modeling for single and fused sensor data, novel data filtering algorithms for detecting various white-box and black-box attacks, revised Deep Neural Network training based on robustness/sensitivity tradeoff in optimization models, evaluation of the impact of sensor fusion, and testing the framework on F1/10 cars and autonomous golf cars. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.