Abstract

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, I have briefly described the data science its evolution its applications in cloud security and how cybersecurity data science came in existence what kind of advantages are given by Cybersecurity Data Science (CSDS) and its steps like, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. After that I have described the various upcoming challenges that can emerge after the frequent applications of CSDS, how machine learning and deep learning are applicable in it and types of algorithms that can be applicable in it. So, the overall paper is not only focuses on the origins of Data Science but it also describes its modern uses for the relevant cybersecurity field and data driven intelligent decision making system can protect our system from known and unknown cyber attacks.

Keywords: Bitcoin, cryptocurrencies, extreme gradient boosting, deep neural networks.