What Is Machine Learning in Cybersecurity?

 




The cyber threat landscape is constantly evolving, with cyber attacks growing in frequency and complexity. According to SecurityWeek, there were twice as many victims of ransomware attacks in 2023 compared to 2022. In fact, Apple reported that there were 20% more data breaches in the first nine months of 2023 than in all of 2022.

According to Statista, the cost of cybercrime across the globe is projected to rise by 50% between 2024 and 2028, growing from $9.22 trillion to $13.82 trillion.

To combat these threats, organizations and cybersecurity professionals rely on an array of tools to facilitate their cyber defense efforts. Machine learning, in particular, has emerged as an invaluable ally in the fight against cybercrime, enabling organizations to be more proactive and efficient in protecting their digital assets.

Cybersecurity professionals and those seeking to break into the field need to understand the growing role of machine learning in cybersecurity and how it can benefit their organizations.
What Is Machine Learning?
Coined by American computer scientist Arthur Samuel in 1959, the term “machine learning” refers to a computer’s capacity to learn without being explicitly programmed, mimicking the way humans learn in a sense.

Machine learning is a subset of artificial intelligence (AI) that uses existing data to train algorithms that can analyze large amounts of data, identify patterns, and project outcomes. This creates a feedback loop, in which machine learning algorithms continuously learn from new data and refine algorithms based on that data, which improves their accuracy.
Types of Machine Learning
There are different models of machine learning, which generally fall into three main categories:

 	
Supervised learning: This model of machine learning uses labeled datasets that train algorithms to organize data and predict outcomes. The algorithm is trained by feeding it input data and the corresponding correct outputs, allowing the model to learn the relationship between inputs and outputs so it can predict future outputs. For example, supervised learning can be used to train algorithms to automatically classify emails as “spam” or “not spam.”
 	
Unsupervised learning: In this type of machine learning, AI developers train algorithms on raw, unlabeled data. Unlike supervised learning, which involves some degree of human guidance, unsupervised learning algorithms are fed unstructured data, which they independently organize and analyze to detect hidden patterns and correlations. The more data the algorithm assesses, the greater its ability to predict outcomes and make decisions.
 	
Reinforcement learning: This form of machine learning relies on trial and error, learning new tasks by being “punished” for incorrect actions and “rewarded” for correct ones. The algorithm is given a set of rules or parameters to follow and then explores different options and possibilities to determine which yields an optimal result.

The Importance of Machine Learning in Cybersecurity
Machine learning is an essential tool in cybersecurity, especially as cybercriminals increasingly employ machine learning and other forms of AI for their own nefarious purposes. For organizations, leveraging machine learning as part of their cybersecurity strategy can yield several advantages.
Data Analysis
To navigate the cyber threat landscape, organizations need to track, organize, and analyze a massive amount of internal and external data points — far more than a team of human analysts could feasibly manage. Machine learning algorithms, however, can process huge datasets with ease. They can also identify patterns and anomalies in the data more efficiently and alert cybersecurity teams of potential cyber hazards.
Task Automation
Cybersecurity professionals can automate numerous cybersecurity tasks — such as implementing security updates, performing penetration testing, and monitoring devices and networks — using machine learning. This not only frees up human analysts to focus on more pressing matters, but it also ensures a level of vigilance and accuracy that’s impossible for cybersecurity teams to match.
Proactive Threat Detection
The speed with which machine learning algorithms process data enables organizations to more easily identify cyber threats in their earliest stages. Additionally, machine learning’s automated threat detection and response capabilities can empower cybersecurity teams to fend off cyber attacks more effectively and mitigate the damage they cause.
Uncovering Network and System Vulnerabilities
Machine learning-powered penetration testing allows organizations to constantly monitor their systems and networks for anomalies and probe for weak points. Machine learning algorithms can also execute software patches, code fixes, and other solutions to fill any security gaps they uncover.
Reducing Security Team Workload and Costs
By automating certain tasks, machine learning relieves security teams of some of the more mundane, repetitive aspects of their jobs and allows them to focus on more strategic or urgent concerns. Additionally, using machine learning to perform these tasks reduces the risk of human error, which can be costly. Delegating some cybersecurity functions to machine learning algorithms can also help organizations cut down on hiring costs — in addition to the money they may save by avoiding cyber attacks altogether or minimizing the damage they cause.
Become a Cybersecurity Expert with TU
Organizations and individuals alike rely on the most advanced cybersecurity tools and techniques to safeguard their digital assets. Using AI, machine learning helps organizations and security teams be more proactive and efficient, allowing them to more effectively defend against cyber threats.

If you want to learn more about the growing role of machine learning in cybersecurity, explore the online M.S. in Cyber Security at The University of Tulsa. One of the first programs of its kind in the U.S., our M.S. in Cyber Security is offered 100% online, with flexible scheduling to accommodate working professionals. We offer both technical and leadership tracks, allowing you to tailor your academic journey to your professional goals.