Improving Cybersecurity with Data Science
February 27, 2020 by Jon O'Keefe

Companies are continually facing cyberthreats and cyberattacks in the dangerous digital world of today. Ideally, a well-equipped cybersecurity team is able to detect, thwart, and manage these threats as they arise, but sometimes they are simply spread too thin in one organization.

 

Thankfully, there’s a new solution in town to help these teams of professionals better handle these types of issues in the cybersecurity landscape: data science.

 

Data scientists analyze and interpret data to help better inform and empower decision makers within a company. They present said data in easily understandable formats through data visualization. This transforms enormous amounts of information into visually engaging charts and graphs as data models to help businesses gain insight into what’s really going on. By empowering cybersecurity professionals with prepared data visualizations that are easy to interpret and useful to make decisions with, data scientists can actually help lighten the load of cybersecurity teams.   

 

The power of data science paired with machine learning can also be harnessed to identify potential cybersecurity threats and help teams work to stop them in their tracks. This can be predicted by data scientists with machine learning automation for outliers and risks based on previous behavior patterns of exploits. This work can help maintain overall security levels by predicting future issues by looking into the past. Through the power of machine learning and the expertise of these teams, patterns can emerge that help companies stay ahead of threats by balancing predictive and reactive methods.

 

For example, fraudulent behavior is one area of cybersecurity where the power of machine learning and data science can make a big difference for a wide variety of companies. Data scientists are able to create regression models that use an Intrusion Detection System to monitor systems for potential attacks. Cyberattacks can also be prevented through Associate Rule Learning conducted by machine learning and data science. Working as a recommendation system, Associate Rule Learning generates responses for specific risks based on a set of characteristics. Threats that may have occurred in the past with the same characteristics help the ARL system understand what a threat may look like while also constantly updating its learned database with evolving or new kinds of cyberattacks.

 

The pairing of data science and cybersecurity is, and will continue to be, a game-changing partnership for companies and organizations around the world. With the power of data-backed decision making, data visualization, and machine learning for automatized, systematic identification of threats, data scientists can help cybersecurity teams improve their practices while also relieving some of their required tasks. This partnership is just getting started for many companies, but we can definitely expect to see much more of it in the future as the power of data science is fully harnessed.