Biblio

One challenge for cybersecurity experts is deciding which type of attack would be successful against the system they wish to protect. Often, this challenge is addressed in an ad hoc fashion and is highly dependent upon the skill and knowledge base of the expert. In this study, we present a method for automatically ranking attack patterns in the Common Attack Pattern Enumeration and Classification (CAPEC) database for a given system. This ranking method is intended to produce suggested attacks to be evaluated by a cybersecurity expert and not a definitive ranking of the "best" attacks. The proposed method uses topic modeling to extract hidden topics from the textual description of each attack pattern and learn the parameters of a topic model. The posterior distribution of topics for the system is estimated using the model and any provided text. Attack patterns are ranked by measuring the distance between each attack topic distribution and the topic distribution of the system using KL divergence.