Intrusion detection is crucial for safeguarding intelligent vehicle systems, aiming to identify abnormal network traffic and operational anomalies. Traditional methods primarily focus on spatial features of attacks, often neglecting temporal dynamics essential for detecting complex, evolving threats. Additionally, the effectiveness of existing techniques is limited by the scope and quality of available datasets, reducing their ability to detect novel, unseen attacks. To address these challenges, this paper introduces a Transformer-based Transfer Learning Intrusion Detection System (TIDS), designed to capture and analyze spatiotemporal sequence features from vehicle data. TIDS generates high-dimensional feature representations of intricate intrusion patterns, improving the detection of known attack types through instance-based transfer learning, enhancing domain adaptability. Moreover, we propose a novel self-supervised box classification method that enhances the system’s capability to detect previously unknown attacks, thereby increasing the overall robustness of the intrusion detection process. Comparative experiments demonstrate that TIDS outperforms traditional methods in detection speed and accuracy across various intrusion scenarios, effectively responding to emerging threats in intelligent vehicle networks.

Vehicular edge computing (VEC) is an efficient solution to alleviate the limitations of local computing resources in vehicular networks. However, the high mobility of vehicles and the dynamic variability of network topologies make it significantly challengeable. In this work, we make a fusion of model-driven and data-driven intelligence to design a multi-agent deep reinforcement learning (DRL) solution for task offloading in urban VEC networks. First, computational models for task queue, transmission, computation, energy consumption, and expense are meticulously developed for the VEC network that integrates communication and computation. Vehicular tasks vary in type, urgency, size, and timeframe, leading to different latency requirements. Tasks may be executed locally within the vehicle, at a server after V2I offloading via cellular communications, or in a neighboring vehicle after V2V offloading via millimeter-wave (mmWave) communications. Each of these options incurs different levels of latency, energy consumption, and expense. Second, based on these models and the utility function that combines latency, energy consumption, and expense, an optimization problem for task offloading is formulated. This problem can be interpreted as a Markov decision process with a carefully designed reward function. Third, to address the offloading problem, we propose a multi-agent proximal policy optimization-based task and target selection algorithm (MAPPO-TTSA). This algorithm also utilizes convolutional neural networks to extract features from large-scale states, thereby enhancing their correlation. Fourth, comprehensive training is performed on the observational data to determine the optimal parameters for predicting task offloading. Finally, extensive experiments are conducted, and simulation results are provided to demonstrate that the proposed intelligent task offloading scheme offers significant advantages in terms of average task completion delay and utility level across various scenarios.

Vehicular platoon, a cutting-edge technology in the realm of intelligent transportation systems, holds the promise of transforming vehicle operations on roadways. By fostering seamless communication among vehicles and leveraging advanced automation, vehicular platoon enables vehicles to travel closely together, thereby reducing aerodynamic drag, optimizing fuel consumption, and improving road safety. However, due to their open and highly dynamic characteristics, the existing platoon communication schemes face efficiency and privacy challenges. These challenges stem from a substantial overhead on the Trusted Authority (TA) and vehicle sides during platoon communication, and a lack of multi-dimensional privacy preservation (e.g., identity privacy, location privacy, attribute privacy, and reputation value privacy) for platoon vehicles. To overcome these challenges, in this paper, we propose an efficient and multi-dimensional privacy-preserving platoon communication (EMPPC) scheme. Specifically, platoon formation is cloud-assisted and relies on the location, attribute, and reputation value of vehicles. We introduce a secure reputation value ciphertext comparison (SRCC) protocol during platoon leader selection, and present an attribute verification and matching (AVM) algorithm during platoon followers matching. Theoretical analysis and simulation evaluation demonstrate that the EMPPC scheme can offer multi-dimensional privacy preservation, and it is secure and efficient for platoon communication.

The field of intelligent connectivity in modern vehicles continues to expand, with vehicle functions becoming increasingly complex over time. This expansion has led to numerous vehicle safety issues, which can easily result in safety failures, leading to injuries and deaths. Therefore, it is particularly important to identify high-risk intelligent connected systems in vehicles, as it can inform safety personnel which systems are most susceptible to safety failures, enabling them to conduct more thorough inspections and tests. In this paper, we develop a new model for vehicle intelligent connected system risk assessment by combining Interval Fuzzy Analytic Hierarchy Process (I-FAHP) with Fuzzy Cluster Analysis (FCA), called VSRQ model. We extract crucial indicators related to vehicle safety, utilizing FCA in conjunction with the I-FAHP to identify vulnerable components in the vehicle intelligent connected system. Priority testing is then conducted on these components to mitigate risks and ensure vehicle safety. We evaluate the model on OpenPilot and provide experimental evidence of the VSRQ model's effectiveness in assessing the safety of vehicle intelligent connected systems. Our experiments fully adhere to the ISO 26262 standard, and our model exhibits a higher accuracy rate compared to other models. These results provide a promising new research direction for predicting the safety risks of vehicle intelligent connected systems and provide typical application tasks for VSRQ. The experimental results show that the accuracy rate is 94.36%, and the recall rate is 73.43%, which is at least 14.63% higher than all other known methods.

We are concerned with the optimal detector design for the all-bit-line MLC flash memory. We provide a channel model of the MLC flash memory, where the channel parameters are mathematically tractable. Then we present an optimal maximum a-posteriori sequence detector. The optimal detector can be executed over a trellis whose branch metrics can be computed by using Fourier transforms of analytically computable characteristic functions (corresponding to likelihood functions). The soft-output detectors for both simple one-dimensional channel models and more realistic page-orientated two-dimensional channel models are derived. Simulation results show not only that the soft-output detector has the same hard-output bit-error-rate performance as some previously known detectors did, but that the soft-output detector outperforms previously known detectors by a gain of 0.23 dB.