相关论文: A neural-network based anomaly detection system an…

Existing intelligent driving technology often has a problem in balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment, and is prone to instability in emergency situations.…

A resilient multi-vehicle system cooperatively performs tasks by exchanging information, detecting, and removing cyber attacks that have the intent of hijacking or diminishing performance of the entire system. In this paper, we propose a…

The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($\delta$ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dynamical systems. We show that suitable sufficient…

Vehicular networks are networks of communicating vehicles, a major enabling technology for future cooperative and autonomous driving technologies. The most important messages in these networks are broadcast-authenticated periodic one-hop…

With the widespread adoption of the Internet of Things (IoT) and Industrial IoT (IIoT) technologies, network architectures have become increasingly complex, and the volume of traffic has grown substantially. This evolution poses significant…

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and…

Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving…

The network security analyzers use intrusion detection systems (IDSes) to distinguish malicious traffic from benign ones. The deep learning-based IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and…

Cooperative Intelligent Transport Systems (C-ITS) create, share and process massive amounts of data which needs to be real-time managed to enable new cooperative and autonomous driving applications. Vehicle-to-Everything (V2X)…

Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by…

Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep neural networks, however, introduce new types of hazards…

Anomaly Detection System (ADS) is an essential part of a modern gateway Electronic Control Unit (ECU) to detect abnormal behaviors and attacks in vehicles. Among the existing attacks, ``one-time`` attack is the most challenging to be…

A modern vehicle contains many electronic control units (ECUs), which communicate with each other through the in-vehicle network to ensure vehicle safety and performance. Emerging Connected and Automated Vehicles (CAVs) will have more ECUs…

Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for…

Intrusion Detection Systems (IDS) are widely employed to detect and mitigate external network security events. Vehicle ad-hoc Networks (VANETs) continue to evolve, especially with developments related to Connected Autonomous Vehicles…

Fleet management systems have been one of the most important research fields in transportation science. Nowadays the enhancement of fleet management systems with technologies such as the Cooperative Intelligent Transport System (CITS) that…

Vehicular ad hoc Networks (VANETs) are emerged mainly to improve road safety, traffic efficiency, and passenger comfort. The performance of most VANET applications relies on the availability of accurate and recent mobility-information,…

The use of deep learning techniques in detecting anomalies in time series data has been an active area of research with a long history of development and a variety of approaches. In particular, reconstruction-based unsupervised anomaly…

Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient…

The progression of innovation and technology and ease of inter-connectivity among networks has allowed us to evolve towards one of the promising areas, the Internet of Vehicles. Nowadays, modern vehicles are connected to a range of…