Forecasting Software Vulnerabilities Using Time-Series Techniques

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Author(s): Baidyanath Biswas (IIM Lucknow, India)
Copyright: 2019
Pages: 41
Source title: Machine Learning Techniques for Improved Business Analytics
Source Author(s)/Editor(s): Dileep Kumar G. (Adama Science and Technology University, Ethiopia)
DOI: 10.4018/978-1-5225-3534-8.ch007

Keywords: Business Information Systems / Business Science Reference / Data Analysis & Statistics / Library & Information Science

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Abstract

This chapter discusses the concepts of time-series applications and forecasting in the context of information systems security. The primary objective in such formulation is the training of the models followed by efficient prediction. Although economic and financial forecasting problems extensively use time-series, predicting software vulnerabilities is a novel idea. The chapter also provides appropriate guidelines for the implementation and adaptation of univariate time-series for information security. To achieve this, the authors focus on the following techniques: autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), and exponential smoothing. The analysis considers a unique data set consisting of the publicly exposed software vulnerabilities, available from the U.S. Dept. of Homeland Security. The problem is presented first, followed by a general framework to identify the problem, estimate the best-fit parameters of that model, and conclude with an illustrative example from the above dataset to familiarize readers with the business problem.

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