Organizations face increasing challenges in addressing and preventing computer and network security incidents. There are financial consequences from security incidents. These include lost time and resources used during recovery, possible theft of personal and/or proprietary information, and reputational damage that may negatively impact stock prices or reduce consumer confidence in a company. Being able to understand and predict trends in computer and network security incidents can aid an organization with resource allocation for prevention of such incidents, as well as evaluation of mitigation strategies. We look at using time series models with a large set of security incident data. We examine appropriateness of the data for modeling and consider needed transformations. Parameter search and model selection criteria are discussed. Then, forecasts from time series models are compared to forecasts from Non-Homogeneous Poisson Process (NHPP) software reliability growth (SRG) models.
['Edward Condon', 'Angela He', 'Michel Cukier']
Analysis of Computer Security Incident Data Using Time Series Models