Biblio

Malware threat classification involves understanding the behavior of the malicious software and how it affects a victim host system. Classifying threats allows for measured response appropriate to the risk involved. Malware incident response depends on many automated tools for the classification of threat to help identify the appropriate reaction to a threat alert. Cuckoo Sandbox is one such tool which can be used for automated analysis of malware and one method of threat classification provided is a threat score. A security analyst might submit a suspicious file to Cuckoo for analysis to determine whether or not the file contains malware or performs potentially malicious behavior on a system. Cuckoo is capable of producing a report of this behavior and ranks the severity of the observed actions as a score from one to ten, with ten being the most severe. As such, a malware sample classified as an 8 would likely take priority over a sample classified as a 3. Unfortunately, this scoring classification can be misleading due to the underlying methodology of severity classification. In this paper we demonstrate why the current methodology of threat scoring is flawed and therefore we believe it can be improved with greater emphasis on analyzing the behavior of the malware. This allows for a threat classification rating which scales with the risk involved in the malware behavior.

With the rapid growth of the cyber attacks, cyber threat intelligence (CTI) sharing becomes essential for providing advance threat notice and enabling timely response to cyber attacks. Our goal in this paper is to develop an approach to extract low-level cyber threat actions from publicly available CTI sources in an automated manner to enable timely defense decision making. Specifically, we innovatively and successfully used the metrics of entropy and mutual information from Information Theory to analyze the text in the cybersecurity domain. Combined with some basic NLP techniques, our framework, called ActionMiner has achieved higher precision and recall than the state-of-the-art Stanford typed dependency parser, which usually works well in general English but not cybersecurity texts.