Paper Contents
Abstract
With the escalating complexity of cyber threats, analyzing incident data sets has emerged as a crucial avenue for enhancing our comprehension of the evolving threat landscape. This research delves into a relatively new and imperative field, focusing on a 12-year span from 2005 to 2017, encapsulating cyber hacking activities. A comprehensive statistical analysis of breach incident data sets forms the core of our investigation. we propose specific stochastic process models tailored to fit the inter-arrival times and breach sizes, crucial dimensions in understanding cyber threat dynamics. Our research demonstrates the efficacy of these models in predicting both inter-arrival times and breach sizes, marking a significant advancement in proactive cyber security measures.Qualitative and quantitative trend analyses are conducted on the dataset, offering nuanced insights into the evolving nature of cyber threats over the studied period. The amalgamation of statistical rigor, stochastic modeling, and trend analyses contributes to a holistic understanding of cyber threat evolution, paving the way for enhanced predictive capabilities and proactive cyber security strategies. This research underscores the urgency for further exploration in this nascent field, recognizing the plethora of opportunities to refine our understanding of cyber threats and fortify digital defenses.
Copyright
Copyright © 2024 Dr Kondragunta Rama Krishnaiah. This is an open access article distributed under the Creative Commons Attribution License.
