An efficient neural network model for time series forecasting of malware

Abstract

Recent research in the computer security sector has primarily been qualitative, focusing on detection aspects where malware has already infected the target. Challenges for security vendors include developing better techniques for early detection of malware attacks before they can do malicious damage. Malware prediction models are needed that can describe and predict malware generation processes. In this study, we address the feasibility of quantitative characterization of malware in security assessment, we propose a model with aim to explain the mechanism behind malware generation which contributes to estimating malware discovery. Although several malware modeling systems have been proposed, such models have shortcomings in related to historical data and do not consider malware data as a time series. Using time series analysis, we provide predictive neural network models for five datasets from Symantec and Malwr. The models explore the structures of malware data along with leveraging non-linear and linear properties to predict the number of future malware. Our examination also reveals that it is possible to model the malware discovery process using a neural network based non-linear model. In addition, our analysis provides insights into understanding the mechanisms that generate malware data series. This information can be useful for intelligence services and vital to threat assessment.