Trustworthy Artificial Intelligence for Securing Transportation Systems
Bhavani M. Thuraisingham · 2024
Artificial Intelligence (AI) techniques are being applied to numerous applications from Healthcare to Cyber Security to Finance. For example, Machine Learning (ML) algorithms are being applied to solve security problems such as malware analysis and insider threat detection. However, there are many challenges in applying ML algorithms for various applications. For example, (i) the ML algorithms may violate the privacy of individuals. This is because we can gather massive amounts of data and apply ML algorithms to the data to extract highly sensitive information. (ii) ML algorithms may show bias and be unfair to various segments of the population. (iii) ML algorithms themselves may be attacked possibly resulting in catastrophic errors including in cyber-physical systems such as transportation systems. Finally, (iv) the ML algorithms must be safe and not harm society. Therefore, when ML algorithms are applied to transportation systems for handling congestion, preventing accidents, and giving advice to drivers, we must ensure that they are secure, ensure privacy and fairness, as well as provide for the safe operation of the transportation systems. Other AY techniques such as Generative AI (GenAI) are also being applied not only to secure systems design but also to determine the attacks and potential solutions. This presentation is divided into two parts. First, we describe our research over the past decade on Trustworthy ML systems. These are systems that are secure as well as ensure privacy, fairness, and safety. We discuss our ensemble-based ML models for detecting attacks as well as our research on developing Adversarial Machine Learning techniques. We also discuss securing the Internet of Transportation systems that are based on traditional methods such as Extended Kalman Filters to detect cyberattacks. Second, Second, we discuss our work on Finally, we discuss the research we recently started as part of the USDOT National University Technology Center TraCR (Transportation Cybersecurity and Resiliency) led by Clemson University. In particular, we describe (i) the application of federated machine learning techniques for detecting attacks in transportation systems; (ii) publishing synthetic transportation data sets that preserve privacy, (iii) fairness algorithms for transportation systems, and (iv) examining how GenAI systems are being integrated with transportation systems to provide security. Our focus includes the following: · Data Privacy: We are designing a Privacy-aware Policy-based Data Management Framework for Transportation Systems. Our work involves collecting the requisite data and developing analysis tools to identify and quantify privacy risks. Existing privacy-preserving, differentially private synthetic data generation techniques, which tailor data utility for generic ML accuracy, are not well suited for specific applications. We are developing synthetic data generation tools for transportation systems applications. We will develop new ML algorithms that can leverage these datasets. · Fairness: We have developed a novel adaptive fairness-aware online meta-learning algorithm, FairSAOML, which adapts to changing environments in both bias control and model precision. Our current work is focusing on adapting our framework to fairness in transportation systems. and control bias over time, especially ensuring group fairness across different protected sub-populations; identifying interesting attributes using explainable AI techniques that might help to mitigate bias and develop equitable algorithms. We have also developed a second system, FairDolce, that recognizes objects involving fairness constraints in a changing environment. We are adapting it to transportation applications. For example, pedestrian detection (whether or not the object being seen is a pedestrian) must be fair with respect to the race or gender of the individuals being detected under changing environments (e.g., rainy, cloudy sunny). Adversarial ML: Our prior work on adversarial ML models worked on traditional datasets such as network traffic data. Our current focus is on adapting our approach to AV-based sensor data. Our ML models are being applied to sensor data for object recognition and traffic management. These ML models may be attacked by the adversary. We will study various attack models and investigate ways of how interactions may occur between the model and the adversary and subsequently develop appropriate adversarial ML models that operate on the AV sensor data. · Attack Detection - Smart vehicles are often exposed to various attacks making it difficult for manufacturers to collaboratively train anomaly/attack detection models. Yet it would be ideal if all the data available across manufacturers could be used in building robust attack detection systems. To achieve this, we developed FAST-SV, which incorporates federated learning in conjunction with augmentation techniques to build a highly performant attack detection system for smart cars. Safety: Safety has been studied for cyber-physical systems and formal methods have been applied to specify safety properties and subsequently verify that the system satisfies the specifications. However, our goal is to ensure that the ML algorithms utilized by the transportation systems are safe. This would involve developing an AI Governance framework that would require transparency and explainability (among others) of the ML algorithms utilized by the transportation system.