Analyzing the data gathered from cyber events is crucial if we are to understand the present state of the threat environment. There are still a lot of unanswered questions in this field of study. Based on malware attacks that occurred between 2005 and 2017, we did a statistical analysis of the data. Contrary to popular belief, we discover that hacking violation case interarrival durations and violation dimensions must be created by stochastic processes, not circulatory systems, as previously thought. This is because of autocorrelations. As a next step, we present stochastic process models for inter-arrival times and violation dimensions. These designs can also forecast the intervals between arrivals and the sizes of violations. For a better understanding of how hacking incidents are progressing, we do both qualitative and quantitative trend assessments on the data set. A current understanding of cyber security shows an increase in the frequency and severity of cyber attacks without an increase in their size.

Cyber Hacking Breaches,Machine Learning, Attacks,Classifications