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.

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.

Collaborative systems frequently depend on various software components, like third-party libraries (TPLs), to execute their functions and expedite the development of the system. The security of an entire collaboration system can be compromised by a TPL that is vulnerable, particularly in an industrial setting. Unfortunately, current TPL detection tools encounter difficulties in precisely identifying version levels and exhibit inefficiency in detecting TPLs on a large scale.To address these challenges, we recommend JHunter, a precise and efficient tool for detecting TPL version details. Our approach involves introducing a novel concept called the attribute class dependency graph (ACDG) as a feature at the package level for TPLs. We then utilise a graph neural network-based method to compare the similarity of ACDGs and identify a list of candidate TPLs. Later, we use more detailed class-level features, such as Control Flow Graphs (CFGs), and constant features to determine version-specific information. We collected 19,095 different versions of TPLs from Maven to build our feature database. Our analysis demonstrates the effectiveness of JHunter on a real-world dataset, achieving F1 scores of 99.34% and 97.28% at the library and version levels, respectively, surpassing previous state-of-the-art (SOTA) results.

Industrial robots are widely used in industrial control systems (ICS). Once compromised, it could be maliciously controlled by attackers, endangering manufacturing processes or even human lives. Therefore, timely discovery of vulnerabilities in industrial robots is essential. Protocol fuzzing is a popular method for discovering protocol implementation vulnerabilities. However, the intricate workflow of industrial robots imposes strict message sequence constraints on message execution. Moreover, the overhead of sequence constraint satisfaction is exacerbated by the redundant messages in message sequences and the inherent delays in physical domain execution. These challenges make it difficult for fuzzers to penetrate deep code paths for fuzzing effectively. In this paper, we propose MSGFuzzer, a message sequence-guided industrial robot protocol fuzzer. Specifically, we filter the original traffic based on message byte characteristics and gener-ate message sequences. After that, we distinguish the sequence constraints for each message through the feedback mechanism of the industrial robot. To reduce state-guidance time, we construct the minimal message sequence based on the constraint conditions of messages. We evaluated MSGFuzzer on a real industrial robot. The results show that MSGFuzzer discovered 12 unique crashes. Note that this is at least 71.4% more effective than state-of-the-art protocol fuzzers in crash discoveries

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.