Accurately identifying network attacks is crucial for maintaining network security. However, these attacks are often hide within massive volumes of network traffic, posing significant challenges for traditional detection methods. Supervised learning approaches require substantial labeled data and struggle to adapt to unknown attack types, while unsupervised methods face difficulties in accurately pinpointing specific attack categories. To address these limitations, we propose a novel fewshot learning model for network flow attack classification based on graph contrastive learning. Our model leverages contrastive learning to enhance feature representation and generalization capabilities, enabling high-accuracy attack detection even with limited training data. Specifically, we first construct a multi- graph representation of network traffic and segment the data into snapshots. Then, we perform graph data augmentation within each snapshot to generate augmented sample pairs, which are used to pre-train the model via contrastive learning. Finally, we fine-tune the model parameters to achieve multi-class attack classification, leveraging the learned feature representations to identify various attack types, even those unseen during training. Experimental results demonstrate that our model exhibits excellent generalization ability and achieves high attack detection performance, even with limited training data.

Internet of vehicles(IOV) is an application of Internet of things in Intelligent Transport System, and has attracted high attention of researchers. IOV brings network connectivity to traditional vehicles, while also introduces security risks. This paper presents experimental analysis on the security of vehicles with Internet connections and propose an approach to Controlling a Car Through OBD Injection. In the experiments, we successfully penetrated several types of cars in a wireless way. We also put out a multi-level safety model of cars, which divides cars into different groups and gives analysis and explanations of each group. All of these things are done for indicating a point of view that traditional cars are not safe enough on information security. It is surely risky to put a car without the ability to resist the attack of informational ways into the Internet of vehicles.

Great attentions have been put on the programming technologies to construct robot software in both academic research and industry due to the increasingly wide applications of robots in various areas, and potential challenges resulting from the complexity of robot software. In the past years, diverse programming technologies and languages have been designed to support the development of robot software. However, with robot applications and requirements change, to develop robot software remains a great challenge, especially when robots are widely used in open environment and expected to provide better and friendly services for human beings. This paper aims at analyzing the technical requirements for designing robot programming language, presenting the roadmap of the robot programming language and discussing its trends and potential challenges.