To enhance the effectiveness of vulnerability detection in software developed using C and C++ programming languages, our study introduces a novel correlation calculation method for analyzing and evaluating Code Property Graphs (CPG). The intelligent computation method proposed in this study comprises three key stages. In the first stage, we present a method for extracting features from the CPG source code. To accomplish this, we integrate three distinct data exploration methods: employing Graph Convolutional Neural (GCN) to extract node features from CPG, utilizing Convolutional Neural Network (CNN) to extract edge features from CPG, and finally employing the Doc2vec natural language processing algorithm to extract source code from CPG nodes. The second stage involves proposing a method for synthesizing CPG source code features. Building on the features acquired in the first stage, our paper introduces a synthesis and construction method to generate feature vectors for the source code. The final stage, stage three, executes the detection of source code vulnerabilities. The experimental results demonstrate that our proposed model in this study achieves higher efficiency compared to other studies, with an improvement ranging from 3% to 4%.

Advanced Persistent Threat (APT) attacks are a form of malicious, intentionally and clearly targeted attack. By using many sophisticated and complicated methods and technologies to attack targets in order to obtain confidential and sensitive information. In fact, in order to detect APT attacks, detection systems often need to apply many parallel and series techniques in order to make the most of the advantages as well as minimize the disadvantages of each technique. Therefore, in this paper, we propose a method of detecting APT attacks based on abnormal behaviors of Network traffic using machine learning. Accordingly, in our research, the abnormal behavior of APT attacks in Network Traffic will be defined on both components: Domain and IP. Then, these behaviors are evaluated and classified based on the Random Forest classification algorithm to conclude about the behavior of APT attacks. Details of the definition of abnormal behaviors of the Domain and IP will be presented in section 3.2 of the paper. The synchronous APT attack detection method proposed in this paper is a novel approach, which will help information security systems detect quickly and accurately signs of the APT attack campaign in the organization. The experimental results presented in section 4 will demonstrate the effectiveness of our proposed method.