Assessing the Intentions and Timing of Malware

Abstract

Malware has become a significant, complex, and widespread problem within the computer industry. It represents one of the most prevalent threats to cybersecurity and is increasingly able to circumvent current detection and mitigation techniques. To help better understand when a malware attack might happen, this article proposes an intention-based classifica- tion of malware and merges it with an optimal timing model to help predict the timing of malware based on its classification. The classification model is based on an examination of eight malware samples, and it identifies four malware classifications and commonalities based on the dimensions of persistence and stealth. The goal of the article is to provide a better understanding of when cyber-conflict will happen, and to help defenders better mit- igate the potential damage.