The Internet of Vehicles (IoV) is highly vulnerable to attacks due to its open communication environment, with new types of attacks continuously emerging. However, existing Network Intrusion Detection Systems (NIDS) often fall short in accuracy, recall rates, false positive rates, and they frequently prove ineffective in detecting new attacks. In this paper, we propose a NIDS for IoV based on Enhanced Dual Gaussian Mixture Variational Autoencoders (EDGMVAE) to detect various attack behaviors, including new attacks. We employ two separate Gaussian Mixture Variational Autoencoders (GMVAE) to conduct unsupervised reconstruction training on normal traffic and known attack traffic. During detection, we obtain the tested traffic’s reconstruction probabilities using these GMVAEs and determine whether an attack has occurred through logical fusion operation. We use two datasets for evaluation, i.e.,the Car Hacking Dataset (CHD) for in-vehicle communication and the UNSW-NB15 Dataset (UBD) for external network communication. The results demonstrate that our method achieves remarkable performance in detecting known attacks, with an accuracy rate of 98.54% on the CHD and 98.59% on the UBD. Moreover, it outperforms other state-of-the-art methods in detecting new attacks. Specifically, on the CHD, the accuracy of the method is 6.66% to 12.83% higher than other methods, while on the UBD, the accuracy is 5.13% to 21.91% higher than other methods.

As Autonomous Driving Systems (ADS) are increasingly deployed, ensuring their safety in edge cases becomes critical to preventing catastrophic failures. However, the limited ADS test scenario diversity often hinders the discovery of new defects, especially in complex and rare situations. This paper presents ScenarioFuzz- Llm,a novel method that leverages Large Language Models (LLMs) to enhance the diversity of ADS test scenarios. By incorporating LLMs into a genetic algorithm-based testing framework, ScenarioFuzz- Llmdirects the mutation to address diversity bottlenecks, thereby enabling the exploration of a broader range of edge cases. Our experiments demonstrate that ScenarioFuzz- Llmenhances the number of violation sce-narios by 10.51 % outperforming the state-of-the-art methods, and uncovers 24 unique defects in ADS, three of which are previously undiscovered. These results highlight the superiority of our approach in enhancing ADS testing through more diverse and comprehensive simulation scenarios, ultimately improving the safety of ADS.

Abnormal driving behavior is a leading cause of road accidents. Traditional detection methods, relying on classification or unsupervised learning, struggle with accuracy and generalization. Deep Reinforcement Learning (DRL) offers potential but faces challenges such as handling unlabeled and imbalanced data, designing effective reward functions, and ensuring efficient exploration. To address these, we propose an expert-guided DRL framework that integrates a CNN-BiLSTM-Self Attention (CBSA) model and an Isolation Forest (iForest) to guide Proximal Policy Optimization (PPO), enhancing detection accuracy and computational efficiency. Our framework consists of two stages. First, a deep learning model trained on labeled data provides expert guidance. Second, unlabeled data is processed through the pre-trained model and iForest, refining the DRL model via a sparse reward function to detect unknown anomalies while mitigating class imbalance and improving generalization. Experiments on an open-source dataset show our method out-performs baselines, achieving the highest recall (0.70) and Fl-score (0.63). Additionally, attention maps and anomaly heatmaps enhance interpretability, confirming its effectiveness for real-time abnormal driving behavior detection and improved driver safety.

Autonomous Driving Systems (ADS) hold significant potential for enhancing travel convenience. Ensuring the reliability of ADS through efficient and comprehensive simulation testing has garnered substantial attention from researchers. In recent years, various search-based automated test scenario generation methods have been proposed to identify potential ADS defects. However, these methods still face challenges in balancing efficiency with comprehensive testing and suffer from a lack of diversity. To address these challenges, we propose AS-Fuzzer, an optimized ADS fuzzing method based on composite traffic scenario generation. AS- Fuzzer introduces a scenario slicing technique based on traffic road structures, allowing each sliced scenario to evolve independently and in parallel, enhancing interaction rates and balancing efficiency with comprehensive testing. A novel scenario generation method Co-Evolutionary Genetic Algorithms (CEGA), is applied within AS-Fuzzer to improve the diversity of generated scenarios, thereby exploring a wider range of ADS defects. Experimental results demonstrate that the proposed method improves test scenario generation efficiency by 120% compared to the state-of-the-art baseline method. Additionally, in the simulation testing of the proposed method, the interaction rate between the ADS vehicle and non-player characters is 2.79 times that of the baseline method, thereby further enhancing ADS testing efficiency. Furthermore, within the same time frame, the proposed method uncovered 19 types of ADS defects that other baseline methods did not explore, achieving higher ADS defect diversity.

The stateful nature inherent in network protocol implementations presents distinctive challenges for testing and verification methods, including Fuzzing. However, not all states hold equal significance. Indiscriminate state selection for fuzzing could lead to intricate path mazes. Similar challenges emerge in the selection for seeds and mutation operators. Therefore, overcoming the efficiency constraints of current fuzzers crucially depends on making precise selections in fuzzing. In this study, we present AcSelector, a novel approach that incorporates the Composite State Model and Mutation Operator Value Table. By offering the most strategic combination of {state, seed, mutation operator}, AcSelector provides systematic guidance for fuzzing, leading to enhanced code coverage. To quantify value of targets, AcSelector employs a principled evaluation strategy. We evaluated AcSelector by fuzzing six network servers from popular open-source projects. Our experimental results demonstrate the effectiveness of AcSelector in increasing code coverage, even under low fuzzing throughput conditions.