Over the past decade, voltage-based intrusion detection systems (IDSs) have faced a persistent and unresolved challenge: models trained by voltage signals collected within a narrow temperature range often fail to generalize effectively to environments with wider temperature variations. This limitation stems from two primary reasons. The voltage characteristics of controller area network bus signals exhibit noticeable variations under different environmental temperatures. Furthermore, although it is feasible to obtain the current environmental temperature of the vehicle, the actual operating temperature of each electronic control unit is difficult to determine. In this paper, we propose a novel scheme named VSSIDS, which responds to the challenge by stretching all training and testing signals to the same amplitude. This mitigates the impact of temperature variations on voltage signals, allowing the trained model to remain effective across environments with wider temperature variations. Our experiments on both a real vehicle and a prototype demonstrate the effectiveness of VSSIDS in intrusion detection and its robustness against temperature variations.

As the evolution of 3GPP specification and the deployment of 5G network, Internet of Vehicles (IoVs) boom fireworks. However, its attack surface is expanded with the increased fusion of various functional interfaces, leading to easier penetration of vehicles. To deal with endless vehicle attacks, scholars propose many methods, where intrusion detection system (IDS) is an important branch. However, many IDSs are based on characteristics of single or specific types of vehicles, which limits model transplantation. Besides, 1-D features are usually utilized in existing IDSs, such as time, traffic, or voltage, etc., limiting the ability to detect attacks related to other dimensions. What is more, many IDSs harness machine learning algorithms and are deployed in vehicles simultaneously, which aggravates the computational burden. Therefore, we devise a cloud-vehicle collaborative IDS based on multidimensional features (CVMIDS) for IoV, called CVMIDS. It solves the problem of data heterogeneity by abstracting different vehicle data to the same feature space. Thus, data sets from different vehicles can be fed into one model for multiclassification, which naturally solves the problem of model transplantation. The feature space is established by combining features in dimensions of time, traffic, and voltage, thereby extending the types of attacks that CVMIDS can detect. Due to the deviated location of abnormal data in feature space compared with normal data, CVMIDS will misclassify vehicle data. Hence, CVMIDS can detect intrusions based on multiclassifying vehicles. Extensive experiments are conducted on three vehicles with different brands and numerical results corroborate the robustness and efficiency of CVMIDS.

As innovative technologies such as autonomous driving, over-the-air technology, and vehicle-to-everything are widely applied to intelligent connected vehicles, people can gain a more convenient and safer driving experience. Although the application of these technologies facilitates our lives, they also bring a series of vulnerable interfaces (such as 5G, Bluetooth, and WiFi), which pose a significant security threat to existing in-vehicle networks. To address these threats, researchers have proposed two mainstream schemes, including message authentication and intrusion detection system (IDS), where the scheme of message authentication needs to occupy the limited bandwidth of controller area network (CAN) bus. Furthermore, most IDSs either cannot locate the sender of the attack, fail to detect aperiodic malicious frames, or require prior knowledge of which CAN identifiers (IDs) belong to which electronic control units (ECUs). To address these weaknesses, we propose a practical voltage-based IDS named IdentifierIDS for real in-vehicle networks. To the best of our knowledge, it is the first scheme to detect intrusions by establishing a voltage fingerprint for each ID without the need for prior knowledge. This allows IdentifierIDS to detect both periodic and aperiodic malicious frames without occupying the limited bandwidth of the CAN bus. As a self-learning IDS, it can adapt to different in-vehicle networks without the need for customization for them. Experiments on three real vehicles demonstrate the robustness of our scheme in different in-vehicle networks.

Intelligent vehicles have become a typical representative of the next generation of technological developments, which provide users with convenient and comfortable driving experiences. However, owing to the factors in weak access control of communication interfaces, lack of authentication for data interaction, and no source/destination address for messages, the controller area network (CAN) is vulnerable to malicious attacks. Due to the resource constraints of bandwidth and the high requirement of real-time data transmission in CAN bus, security solutions, such as in-vehicle gateways and firewalls, cannot be endowed with complex encryption authentication algorithms, contributing to the limited capabilities in security protection. Therefore, some scholars proposed intrusion detection systems (IDSs) based on side-channel analysis. However, most of the existing detection models are based on single-class side-channel features to detect limited types of attacks. How to design a robust, lightweight, highly real-time, and traceable IDS is a major problem in the field of vehicle security. In view of this, we design a new IDS based on multiple features, called MIDS. We have conducted numerous experiments on two different brands of vehicles for three typical attacks, and MIDS has a detection accuracy of more than 98% and detects per frame within 0.12ms.

With the popularity of intelligent and connected vehicles, driver identification based on driver behaviors, which has great significance in driving safety, vehicle alarm system and other situations, attracts more and more attention. However, the implementation of driver identification also faces some problems, such as insufficient computing power, slow transmission rate and so on. In order to solve these problems, people use mobile edge computing technology to transfer the computing process, which makes drive identification more feasible in practical scenarios. Many researchers have done a lot of works for the accuracy of driver identification. Nonetheless, few of them pay attention to the identification efficiency in the scenarios where drivers are constantly added to the model. Different from the works before, we propose a new driver identification scheme based on incremental learning for the first time, which can greatly improve the efficiency of model retraining when new driver is added. We extract the driver behavior characteristics data from two physical vehicles and train an incremental learning model in edge server. The experimental results show that the our scheme can accurately identify drivers. With the number of drivers in model increasing, although the identification accuracy slowly decreases, it remains above 93%, which means our scheme against the catastrophic forgetting well. Comparing with the existing identification schemes, our scheme requires less time and memory resources for model retraining, which is suitable for practical scenarios.

The appearance of autonomous vehicle prompts the development of multi-access edge computing. In cellular vehicle-to-everything (C-V2X) system, autonomous vehicle can offload complicated tasks to multi-access edge server (MES) due to the shortage of computation resource. However, because of eavesdroppers, there exists security problem in wireless communication between vehicles and MES. Except for security problem, autonomous vehicle also casts stringent requirements on latency and energy consumption for task offloading, which motivates plenty of scholars to study. Most of the existing studies cover one or two dimensions of aforementioned three problems, which limits their practicability. Therefore, we put forward a multi-access edge task offloading scheme with carefully considering information security, offloading latency and energy consumption. To solve the security problem at physical layer, small-cell base station proactively sends artificial noises to degrade the decoding ability of wiretappers. On the premise of secure wireless communication, the optimal task offloading proportion is calculated through one dimension search algorithm in the scheme, which ensures the minimum weighted sum of offloading latency and energy consumption. Numerical results validate the feasibility and effectiveness of the proposed scheme.