Data science and cybersecurity have become closely knit in the age of digitalization and the Internet of Things (IoT). Determining and developing the right security methods to counter threats and attacks entails a scientific collection and analysis of various security incident information. Data science complements the advancement of security tools and strategies.
This growing connection is particularly true in cyber risk assessment automation and continuous security testing. Automated cybersecurity and ceaseless testing require machine learning and the adroit handling of security data.
It is worth noting that data science and cybersecurity appear to be following the same popularity trajectory. Based on Google Trends information from October 2014 through August 2019, the topics of data science and cybersecurity, along with machine learning, have trended in almost the same way. This interesting relationship merits mindful scrutiny.
The importance of data science in cybersecurity
A paper on cybersecurity data science published in the Journal of Big Data explores the impact of data science on the changes in security technologies. “In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change,” the authors state.
Penned by researchers from Australian universities, the paper points out the important role of data science in gathering and analyzing data to obtain insights or patterns in security incidents. These insights are then used to produce data-driven models for the development of cybersecurity solutions.
Data science in cybersecurity, according to the researchers, makes the computing process more actionable and intelligent as compared to the conventional handling of information when developing cybersecurity solutions. It provides a guide for the collection of data from relevant cybersecurity sources and analytics that complements data-driven patterns.
Simply put, data science is essential in developing cybersecurity solutions based on the massive amounts of historical, current, and emerging threat information. Without the organization, scientific processes, and algorithms that come with data science, it would be difficult to make sense of the
How data science changes cybersecurity
The cybersecurity data science paper mentioned earlier suggests that the fusion of data science and cybersecurity depicts a partial paradigm shift from traditional security solutions such as user authentication, access control, cryptography, and firewalls. This shift is necessary because conventional security principles are deemed inapplicable or ineffective in the current cyber industry needs.
Before data science became widely adopted in the cybersecurity field, security solutions were developed with a statistical approach by a few experienced security analysts. They have data management as part of the process, but it is handled in an ad-hoc manner. In other words, data management is largely reactive and improvised. There is no systemized approach in place. If there is any, it is only for a limited or specific application.
As cyber-attacks increased exponentially, cyber threat information similarly exploded. Security analysts are faced with the advantage of having information about threats, but they are unable to make the most out of the endless stream of data. As such, the idea of having a few teams of security experts working on the development of security solutions is quickly becoming unviable.
It’s either they increase the number of people working on a cybersecurity project, or they embrace automation. Enlisting the help of more security experts can be done with lower costs through crowdsourcing, but it is not sustainable in the long run given the massively increasing amounts of cybersecurity threats and attacks every year. Not to mention, it’s not only the volume of attacks that increases. As Microsoft reported, the sophistication of attacks is also stepping up.
Data science is forging a new scientific paradigm that is changing the cybersecurity landscape, and this change is positive by all accounts. In particular, security professionals are adjusting the way they process security information in line with the principles of data science. They adopt systematic ways to gather and organize data while improving analytics to extract insights that help develop more effective security solutions.
Additionally, data science is involved in the application of machine learning methods particularly when it comes to automation. Cybersecurity systems already incorporate automation to accelerate the analysis of threats and hasten intervention during attacks. Incidentally, this automation requires machine learning and algorithms. Without data science, it is nearly impossible to build meaningful data-driven models and implement useful machine learning.
The need for a multi-layered framework
It helps to establish a multi-layered framework to guide the widespread adoption of data science in cybersecurity development. As mentioned, even crowdsourcing is not enough to counter the tremendous growth of cyber threats not only in terms of volume but also in sophistication. Collaboration among security professionals needs to be coordinated to amplify outcomes and keep up with the massive number of attacks. Sorting, analyzing, and utilizing security incident data will be expedited with a framework in place.
Having a framework aids in the development of smart cybersecurity solutions. Data science, after all, is not just about collecting and parsing data. It also involves the application of machine learning methods, the quantification of cyber risks, and the adoption of inferential techniques to discover behavioral patterns. Existing cybersecurity frameworks, guidelines, or best practices will need tweaks or adjustments.
It is essential to take into account the incremental learning nature and dynamism in the cybersecurity field. Every second, new security information emerges. Security professionals do not respond to these in bulk or batches. They dynamically study and resolve the threats. For this, data science protocols may need adjustments.
The standard data collection, preparation, and machine learning modeling layers for cybersecurity infrastructure will require another layer to take dynamism and incrementality into account. This means adding a layer where recency mining, post-processing, and improvements, as well as response planning and decision-making protocols are added.
The bottomline
The shift in cybersecurity development comes with changes that require security professionals to learn new skills and methods. They may not need to become expert data scientists, but they have to comprehend the implications of infusing data science into security development.
An understanding of machine learning techniques such as data clustering, feature engineering, association analysis, classification, and neural networks significantly improves the appreciation of security data and the development of automated cyber risk assessment and cybersecurity solutions.
