The figure represents timestamp information in terms of a particular date in the x-axis and corresponding popularity in the range of 0 (minimum) to 100 (maximum) in the y-axis. 1, based on the data of the last five years collected from Google Trends. The popularity of these related technologies is increasing day-by-day, which is shown in Fig. Machine learning can significantly change the cybersecurity landscape and data science is leading a new scientific paradigm.
In recent days, cybersecurity is undergoing massive shifts in technology and its operations in the context of computing, and data science (DS) is driving the change, where machine learning (ML), a core part of “Artificial Intelligence” (AI) can play a vital role to discover the insights from data. Therefore, to effectively identify various cyber incidents either previously seen or unseen, and intelligently protect the relevant systems from such cyber-attacks, is a key issue to be solved urgently.Ĭybersecurity is a set of technologies and processes designed to protect computers, networks, programs and data from attack, damage, or unauthorized access. According to, the national security of a country depends on the business, government, and individual citizens having access to applications and tools which are highly secure, and the capability on detecting and eliminating such cyber-threats in a timely way. Thus, it’s essential that organizations need to adopt and implement a strong cybersecurity approach to mitigate the loss. According to Juniper Research, the number of records breached each year to nearly triple over the next 5 years. It’s estimated that, a data breach costs 8.19 million USD for the United States and 3.9 million USD on an average, and the annual cost to the global economy from cybercrime is 400 billion USD. Cybercrime and attacks can cause devastating financial losses and affect organizations and individuals as well. By 2012, they were double around 100 million, and in 2019, there are more than 900 million malicious executables known to the security community, and this number is likely to grow, according to the statistics of AV-TEST institute in Germany. For instance, in 2010, there were less than 50 million unique malware executables known to the security community. have grown at an exponential rate in recent years. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.ĭue to the increasing dependency on digitalization and Internet-of-Things (IoT), various security incidents such as unauthorized access, malware attack, zero-day attack, data breach, denial of service (DoS), social engineering or phishing etc. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. We then discuss and summarize a number of associated research issues and future directions. 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. In this paper, we focus and briefly discuss on cybersecurity data science, 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.
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. 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. In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change.