Biblio

Code-graph based software defect prediction methods have become a research focus in SDP field. Among them, Code Property Graph is used as a form of data representation for code defects due to its ability to characterize the structural features and dependencies of defect codes. However, since the coarse granularity of Code Property Graph, redundant information which is not related to defects often attached to the characterization of software defects. Thus, it is a problem to be solved in how to locate software defects at a finer granularity in Code Property Graph. Static analysis is a technique for identifying software defects using set defect rules, and there are many proven static analysis tools in the industry. In this paper, we propose a method for locating specific types of defects in the Code Property Graph based on the result of static analysis tool. Experiments show that the location method based on static analysis results can effectively predict the location of specific defect types in real software program.

The emergence of the Internet of Things (IoT) is not only a global revolution in the information industry, but also brought tremendous changes to our lives. With the development of the technology and means of the IoT, information security issues have gradually emerged, and intrusion attacks have become one of the main problems of the IoT network security. The network layer of the IoT is the key connecting the platform and sensors or controllers of the IoT, and it is also the most standardized, the strongest and the most mature part of the whole physical network architecture. Its large-scale development has led to the network layer's security issues will receive more attention and face more challenges. This paper proposes an intrusion detection algorithm deployed on the network layer of the IoT, which uses the BPSO algorithm to extract features from the NSL-KDD dataset, and applies support vector machines (SVM) as the core model of the algorithm to detect and identify abnormal data, especially DoS attacks. Experimental results show that the model's detection rate of abnormal data and DoS attacks are significantly improved.