X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

Assessing the structural vulnerability of online social networks has been one of the most engaging topics recently, which is quite essential and beneficial to holding the network connectivity and facilitating information flow, but most of the existing vulnerability assessment measures and the corresponding solutions fail to accurately reveal the global damage done to the network. In order to accurately measure the vulnerability of networks, an invulnerability index based on the concept of improved tenacity is proposed in the present study. Compared with existing measurements, the new method does not measure a single property performance, such as giant component size or the number of components after destruction, but pays special attention to the potential equilibrium between the removal cost and the removal effect. Extensive experiments on real-world social networks demonstrate the accuracy and effectiveness of the proposed method. Moreover, compared with results of attacks based on the different centrality indices, we found an individual node’s prominence in a network is inherently related to the structural properties of network. In high centralized networks, the nodes with higher eigenvector are more important than the others in maintaining stability and connectivity. But in low centralized networks, the nodes with higher betweenness are more powerful than the others. In addition, the experimental results indicate that low centralized networks can tolerate high intentional attacks and has a better adaptability to attacks than high centralized networks.