Machine learning and AI

Cybersecurity Data Science

Introduction

Boiled down to its most simple explanation, data science offers understanding. Everyone in every industry would like to better understand the landscape they operate in, and cybersecurity is no exception. It takes understanding to be able to anticipate and respond to attacks, threats and other cybersecurity issues. 

This article will detail the state of cybersecurity before the impact of data science, as well as the many ways that data science can be used in cybersecurity. Data science is used in intrusion detection, behavioral analytics, data protection and forsaking laboratory scenarios for real-world examples. This article will conclude with a well-founded recommendation for how cybersecurity data science can improve with one small change.

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Cybersecurity before data science

The most accurate description that I have come across about cybersecurity before data science is that of FUD, or fear, uncertainty and doubt. A significant number of decisions were made, right or wrong, based upon assumptions: assumptions about how attackers may attack, where they may attack and more. Assumptions were potentially the core of many organizations’ information security strategies. 

Data science has effectively illuminated this previously dark space of FUD-based assumptions and has moved the cybersecurity playing field towards basing decisions more on facts. This does not mean the job of cybersecurity is now a cakewalk that cybersecurity analysts and information security professionals can basically ignore. Rather, they now have the data-driven methods and tools that will allow them to do their jobs better.

Currently, many organizations service their cybersecurity data science needs by hiring outside consultants to lend their expertise. This has proven to be exceedingly insightful for many organizations, as they now have a more realistic vision of their own cybersecurity risks and strengths. These external consultants generally do not collaborate much with organizational security teams.

Some ways that data science has changed cybersecurity

The effect that data science has had on cybersecurity has been profound, revolutionizing in ways not yet known. Below is a list of some big ways that data science has benefited cybersecurity.

Better intrusion detection and prediction

Cybersecurity is held to be a classic game of cat-and-mouse, and this analogy could not be any more on point. Attackers and hackers use a wide range of intrusion styles, methods and tools that can change at any time. Earlier intrusion detection system helped narrow the gap of space and time between incident and response, but the playing field was still heavily skewed in favor of attackers.

With the implementation of data science, both historical and present information about these intrusions can now be fed into machine learning algorithms. The end result of this is a more precise intrusion detection and management system and one that can predict future attacks before they happen. Machine learning algorithms can even assist in spotting loopholes within an information security environment, thereby further tightening the organization’s security.

Behavioral analytics

Intrusion detection and identifying malware is one thing but understanding attacker behavior is quite another. Data science allows for the reliable analysis of vast massive amounts of information, particularly data from organizations. 

New tools, such as Solarwinds Log and Event Manager (LEM), use behavioral analytics to pull enormous amounts of data from multiple data sources. Relevant system and network logs are included then correlated to predictive future behavior. 

This is the gold at the end of the AI rainbow in cybersecurity. Soon, so much information will be processed that malicious actors will be far easier to handle. 

Data protection

Data science has also assisted in better data protection. Previously-used security measures, including complex signatures and encryption, have helped stop information probing and other methods attackers use when attacking extremely valuable and sensitive information. Data science steps up to the plate and reinforces these previous measures by allowing for impenetrable protocols with highly developed machine learning algorithms. 

Forsaking laboratory scenarios for real-world scenarios

Another massive improvement brought on by data science is being able to move away from laboratory scenarios and hypotheticals for real-world examples. These real-world examples are pulled from historical data that algorithms use to show what has happened in previous examples of attacks and how the organization responded and recovered. This can be used by organizations to gain the truest idea of their information security landscape, and this self-awareness will pay dividends in the way of better security. 

Conclusion

As explored above, data science has already had a massive positive impact on cybersecurity during its short history. A strong positive aspect of data science is the fact that as time goes on, more information is analyzed, meaning that better predictions can be made and putting attackers and cybercriminals on their back feet.

Despite these improvements to cybersecurity, its relationship with data science can be further improved with one small change as I mentioned earlier. This small modification is for data scientists to directly work with security teams within organizations. The end result will be an even better understanding of their collective security environment and, most likely, improved methods of using data science in cybersecurity. 

Learn Cybersecurity Data Science

Build your skills using machine learning and other cutting-edge tools to perform various cybersecurity tasks.

Start Learning

 

Sources

1. How data science can answer cybersecurity challenges, JAXenter
2. Role of Data Science in Cyber Security, Dataversity
3. Data science is changing how cybersecurity teams hunt threats, Silicon Republic
4. Why Data Science Is Becoming So Important in Cybersecurity, insideBIGDATA