Automotive Ethernet is considered to be the next-generation in-vehicle network, because of its high bandwidth, high throughput, and low cost characteristics. However, no common standard has been established for the security protocol of Automotive Ethernet. While there are a few candidates, including MACsec, IPsec, and TLS, there is no widely favored candidate. Most importantly, existing candidates cannot fully satisfy the requirements of in-vehicle communication, specifically source authentication for broadcast/multicast communication. In this paper, we conduct a comprehensive analysis in both security and performance of existing security protocol candidates and identify source authentication and Denial-of-Service (DoS) prevention as two essential but missing properties in these candidates. We propose Gatekeeper, a gateway-based broadcast authentication protocol to ensure source authentication. In general, Gatekeeper introduces an on-path authenticator, which co-locates with the in-vehicle gateway or domain controllers and helps receivers to verify the sender's identity. To defend against DoS threats, we further integrate the time-lock puzzle with Gatekeeper to slow down malicious traffic. Our performance evaluation results show that Gatekeeper only results in 0.03 ms latency overhead for CAN data transmission and outperforms TESLA on both CAN and LiDAR transmission scenarios, highlighting the effectiveness and efficiency of Gatekeeper.

Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation. However, few studies have analyzed the adversarial robustness of trajectory prediction or investigated whether the worst-case prediction can still lead to safe planning. To bridge this gap, we study the adversarial robustness of trajectory prediction models by proposing a new adversarial attack that perturbs normal vehicle trajectories to maximize the prediction error. Our experiments on three models and three datasets show that the adversarial prediction increases the prediction error by more than 150%. Our case studies show that if an adversary drives a vehicle close to the target AV following the adversarial trajectory, the AV may make an inaccurate prediction and even make unsafe driving decisions. We also explore possible mitigation techniques via data augmentation and trajectory smoothing.

Superseding HTTP/1.1, the dominating web protocol, HTTP/2 promises to make web applications faster and safer by introducing many new features, such as multiplexing, header compression, request priority, server push, etc. Although a few recent studies examined the adoption of HTTP/2 and evaluated its impacts, little is known about whether the popular HTTP/2 servers have correctly realized the new features and how the deployed servers use these features. To fill in the gap, in this paper, we conduct the first systematic investigation by inspecting six popular implementations of HTTP/2 servers (i.e., Nginx, Apache, H2O, Lightspeed, nghttpd and Tengine) and measuring the top 1 million Alexa web sites. In particular, we propose new methods and develop a tool named H2Scope to assess the new features in those servers. The results of the large-scale measurement on HTTP/2 web sites reveal new observations and insights. This study sheds light on the current status and the future research of HTTP/2.

By hiding messages inside existing network protocols, anti-censorship tools could empower censored users to visit blocked websites. However, existing solutions generally suffer from two limitations. First, they usually need the support of ISP or the deployment of many customized hosts to conceal the communication between censored users and blocked websites. Second, their manipulations of normal network traffic may result in detectable features, which could be captured by the censorship system. In this paper, to tackle these limitations, we propose a novel framework that exploits the publicly available automation services and the plenty of web services and contents to circumvent web censorship, and realize it in a practical tool named AutoFlowLeaker. Moreover, we conduct extensive experiments to evaluate AutoFlowLeaker, and the results show that it has promising performance and can effectively evade realworld web censorship.