Traffic congestion not only wastes valuable time but also exacerbates greenhouse gas emissions. We envision a future city where smart traffic management systems are capable of eliminating traffic jams entirely through coordinated traffic planning, with connected autonomous vehicles interleaving each other without collisions. The state-of-the-art automatic traffic plannings focus on improving traffic throughput of single intersections, however, these approaches may not be optimal for multiple intersections. To the best of our knowledge, there is currently no algorithm addressing large-scale irregular multi-intersection road networks for overall traffic optimization. In this work, we propose UrbanTrafficNet, one of the first AI assisted large-scale traffic management plan generation mechanism. The UrbanTrafficNet employs a unique algorithm to encode dynamic vehicle status and complex road network across multiple intersections. This information is then fed into novel deep learning-based models which generate detailed traffic plans for individual vehicles. The UrbanTrafficNet is universally applicable to any complicated layouts of urban road networks and achieves near-maximum traffic throughput. Compared to traditional traffic light scheduling, UrbanTrafficNet reduces stop-and-go percentages by up to 80%, resulting in smoother traffic flow and decrease in greenhouse gas emissions. Extensive real-world experiments validate the effectiveness and efficiency of our proposed system, showcasing its practicality in addressing traffic congestion and environmental concerns.

With the continuous advancement of sensing, networking, controlling, and computing technologies, there is a growing number of IoT (Internet of Things) devices emerging that are expected to integrate into public infrastructure in the near future. However, the deployment of these smart devices in public venues presents new challenges for existing access control mechanisms, particularly in terms of efficiency. To address these challenges, we have developed a highly efficient and scalable access control mechanism that enables automatic and fine-grained access control management while incurring low overhead in large-scale settings. Our mechanism includes a dual-hierarchy access control structure and associated information retrieval algorithms, which we have used to develop a large-scale IoT device access control system called FACT+. FACT+ overcomes the efficiency issues of granting and inquiring access control status over millions of devices in pervasive environments. Additionally, our system offers a pay-and-consume scheme and plug-and-play device management for convenient adoption by service providers. We have conducted extensive experiments to demonstrate the practicality, effectiveness, and efficiency of our access control mechanism.

Ransomware attacks have become widespread in the last few years and have affected many critical industries and infrastructures. Unfortunately, there are no recovery tools that can effectively defend against all types of ransomware. Approaches, such as frequent data backups, have several drawbacks. They are expensive in terms of resources and trained technical staff. Therefore, it is much more challenging and cost-consuming for average users and small business owners to survive ransomware attacks. To provide an easy-to-use tool for a broader population of users and businesses, we propose a novel ransomware defense mechanism that can be conveniently deployed in modern Windows systems which have over 76% market share as of 2022. The uniqueness of our approach is to fight malware like malware. We leverage Alternate Data Streams, which are sometimes used by malicious applications, to design and implement a data protection method that misleads the ransomware into attacking only file “shells” instead of the actual file content. We have evaluated our approach against different cryptographic ransomware. The results show that our approach is usable, efficient, and effective.

With the advances in autonomous vehicles and intelligent intersection management systems, traffic lights may be replaced by optimal travel plans calculated for each passing vehicle in the future. While these technological advancements are envisioned to greatly improve travel efficiency, they are still facing various challenging security hurdles since even a single deviation of a vehicle from its assigned travel plan could cause a serious accident if the surrounding vehicles do not take necessary actions in a timely manner. In this paper, we propose a novel security mechanism namely NWADE which can be integrated into existing autonomous intersection management systems to help detect malicious vehicle behavior and generate evacuation plans. In the NWADE mechanism, we introduce the neighborhood watch concept whereby each vehicle around the intersection will serve as a watcher to report or verify the abnormal behavior of any nearby vehicle and the intersection manager. We propose a blockchain-based verification framework to guarantee the integrity and trustworthiness of the individual travel plans optimized for the entire intersection. We have conducted extensive experimental studies on various traffic scenarios, and the experimental results demonstrate the practicality, effectiveness, and efficiency of our mechanism.