Security attack prediction based on user sentiment analysis of Twitter data

In recent years, security attacks on the web have been perpetrated by hacker activist organizations that aim to destabilize (using different techniques) web services in a specific context for which they are motivated. Predicting these attacks is an important task that helps to consider what actions should be taken if the attack is latent. Although there are applications to detect security threats on the web, currently there is no system that can predict or forecast whether the attacks can reach consummation. This paper presents a sentiment analysis method on Twitter content to predict future attacks on the web. The method is based on the daily collection of tweets from two sets of users; those who use the platform as a means of expression for views on relevant issues, and those who use it to present contents related to security attacks in the web. Daily information is converted into data that can be analysed statistically to predict whether there is a possibility of an attack. The latter is done by analyzing the collective sentiment of users and groups of hacking activists in response to a global event.