Abstract: With the popularity of the Internet, people can more easily obtain information from the network and interact with the outside world via the Web in anytime or anywhere. With the access to information easily, security issues come out, such as information disclosure, account passwords stolen and so on, which rise more and more public concern on information security. Nowadays Web news is one of main social media, which contains a large number of public concerning issues, such as information security events. However, security information is often buried in the mass of Web documents, making it inconvenient to quickly obtain recent information security events for readers. Therefore, establishing a method to automatic extracting information security events is significant. In this paper, we regard single sentence as “information security” unit, applying machine learning algorithm to determine whether a sentence containing “information security events” or not. Sentences containing “information security events” are extracted from news documents as the desired results. Via manual training data construction, sentence feature designing and support vector machine (SVM) model training, we propose an automatic method to extract “information security” related sentences from news documents. Experiment result show that, the method discussed in this paper get high precision and recall in information security events discovery, which verify the effectiveness of proposed method.