A Vine Copula Model for Predicting the Effectiveness of Cyber Defense Early-Warning

abstract

Internet-based computer information systems play critical roles in many aspects of modern society. However, these systems are constantly under cyber attacks that can cause catastrophic consequences. To defend these systems effectively, it is necessary to measure and predict the effectiveness of cyber defense mechanisms. In this article, we investigate how to measure and predict the effectiveness of an important cyber defense mechanism that is known as early-warning. This turns out to be a challenging problem because we must accommodate the dependence among certain four-dimensional time series. In the course of using a dataset to demonstrate the prediction methodology, we discovered a new nonexchangeable and rotationally symmetric dependence structure, which may be of independent value. We propose a new vine copula model to accommodate the newly discovered dependence structure, and show that the new model can predict the effectiveness of early-warning more accurately than the others. We also discuss how to use the prediction methodology in practice.

KEYWORDS:

• Copula-GARCH
• Cybersecurity
• Prediction

Acknowledgments

The authors thank the editor, associate editor, and anonymous reviewers for their constructive comments that have guided us in improving our article. The authors thank CAIDA for providing us the dataset that is analyzed in the present article. This research was approved by IRB. The authors thank Sajad Khorsandroo for preprocessing the data and Marcus Pendleton for proofreading the article. This research was supported in part by ARO Grant #W911NF-13-1-0141.