Improving Zero-Day Malware Testing Methodology Using Statistically Significant Time-Lagged Test Samples
This addresses the problem of accurately testing security products for enterprises, but it is incremental as it improves existing evaluation methodologies rather than introducing a new detection paradigm.
The paper tackles the challenge of evaluating zero-day malware detection by showing that millions of benign files and tens of thousands of malware samples are needed for accurate assessment, and proposes a time-delay method to collect labeled samples cheaply for better true and false positive rate measurement.
Enterprise networks are in constant danger of being breached by cyber-attackers, but making the decision about what security tools to deploy to mitigate this risk requires carefully designed evaluation of security products. One of the most important metrics for a protection product is how well it is able to stop malware, specifically on "zero"-day malware that has not been seen by the security community before. However, evaluating zero-day performance is difficult, because of larger number of previously unseen samples that are needed to properly measure the true and false positive rate, and the challenges involved in accurately labeling these samples. This paper addresses these issues from a statistical and practical perspective. Our contributions include first showing that the number of benign files needed for proper evaluation is on the order of a millions, and the number of malware samples needed is on the order of tens of thousands. We then propose and justify a time-delay method for easily collecting large number of previously unseen, but labeled, samples. This enables cheap and accurate evaluation of zero-day true and false positive rates. Finally, we propose a more fine-grain labeling of the malware/benignware in order to better model the heterogeneous distribution of files on various networks.