RSU-Based Online Intrusion Detection and Mitigation for VANET

Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting the integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel statistical intrusion detection and mitigation techniques based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods are evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior performance of the proposed methods in terms of quick and accurate detection and localization of cyberattacks

Towards Efficient and Secure Intelligent Transportation Services: AI-driven Traffic Light Controller and Privacy-Preserving Mobility Data Generation

The widespread adoption of artificial intelligence (AI) and Intelligent Transportation Systems (ITS) technologies has led to the increasing application of AI-based ITS controllers, with the Traffic Signal Controller (TSC) being a prominent example. Reinforcement learning (RL) models have shown promising results for adaptively adjusting traffic light schedules in urban environments through RL-based TSCs (RL-TSCs). The real-world deployment of RL-TSCs involves three key aspects: performance, security, and data privacy. In terms of performance, RL-TSC models need to be designed with consideration for various metrics, such as fair traffic scheduling and air quality impact. To address this, our approach takes into account a multi-objective constrained learning formulation to optimize performance. However, the use of RL-TSCs for automation, by leveraging external inputs, introduces security concerns that require active research to mitigate. We address these security challenges by introducing an innovative defense mechanism. Additionally, the training of RL-TSCs relies on real-world mobility datasets, necessitating the protection of data privacy at different levels of granularity. To minimize the constraints associated with limited real data availability or privacy concerns, we introduce two distinct directions: synthetic trajectory data generation using recent generative AI methods, and location privacy models for raw mobility datasets based on differential privacy, which safeguard individual trajectories and aggregated mobility datasets.This research provides a valuable tool for evaluating the practical deployment of RL-TSCs, particularly in real-world settings where the last mile of implementation and security is paramount. By addressing the key challenges of performance, security, and data privacy, this work aims to facilitate the successful real-world deployment of AI-powered ITS controllers

Statistical Anomaly Detection and Mitigation of Cyber Attacks for Intelligent Transportation Systems

Secure vehicular communication is a critical factor for secure traffic management. Perfect security in intelligent transportation systems (ITS) has solid and efficient intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-ofservice attacks (DDoS), especially the stealth low-rate DDoS attacks, targeting the integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel statistical intrusion detection and mitigation techniques are proposed for the considered attacks. The performance of the proposed methods are evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior performance of the proposed methods in terms of quick and accurate detection and localization of cyber-attacks

RSU-Based Online Intrusion Detection and Mitigation for VANET

Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability

RSU-Based Online Intrusion Detection and Mitigation for VANET.

Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability

Improving Security and User Privacy in Learning-Based Traffic Signal Controllers (TSC)

Final ReportThe 21st century of transportation systems leverages intelligent learning agents and data-centric approaches to analyze information gathered with sensing (both vehicles and roadsides) or shared by users to improve transportation efficiency and safety. Numerous machine learning (ML) models have been incorporated to make control decisions (e.g., traffic light control schedules) based on mining mobility data sets and real-time input from vehicles via vehicle-to-vehicle and vehicle-to-infrastructure communications. However, in such situations, where ML models are used for automation by leveraging external inputs, the associated security and privacy issues start to surface. This project aims to study the security of ML systems and data privacy associated with learning-based traffic signal controllers (TSCs). Preliminary work has demonstrated that deep reinforcement learning (DRL) based TSCs are vulnerable to both white-box and black-box cyber-attacks. Research goals include 1) quantifying the impact of such security vulnerabilities on the safety and efficiency of the TSC operation, and 2) developing effective detection and mitigation mechanisms for such attacks. In learning based TSCs, vehicles share their messages with the DRL agents at TSCs, which will then analyze the data and take action. Sharing vehicular mobility data with a network of TSCs may cause privacy leakage. To address this problem, differential privacy techniques will be applied to the mobility datasets to protect user privacy while preserving the effectiveness of the prediction outcomes of traffic-actuated or learning-based TSC algorithms. Approaches will be evaluated in vehicular simulators using real mobility data from San Francisco and other cities in California. By accomplishing these goals, learning-based transportation systems will be more secure and reliable for real-time implementations.U.S. Department of Transportation 69A355174711