In order to actively respond to cyber attacks, not only the security systems such as IDS, IPS, and Firewalls, but also ESM, a system that detects cyber attacks by analyzing various log data, are preferably deployed. However, as the attacks be come more elaborate and advanced, existing signature-based detection methods start to face their limitations. In response to that, researches upon symptom detection technology based on attack modeling by employing big-data analysis technology are actively on-going. This symptom detection technology is effective when it can accurately extract features of attacks and manipulate them to successfully execute the attack modeling. We propose the ways to extract attack features which can play a role as the basis of the modeling and detect intelligent threats by carrying out scenario-based modeling.