The company’s attack surface is huge and continues to increase and evolve quickly. However, depending on the size of your company, there are almost several hundred billion time-varying signals.
These signals require to be analyzed to calculate risk properly. As a result, analyzing and improving cyber security posture is more than a human-scale problem now.
Due to this unprecedented challenge, machine learning in cyber security has emerged to help information security teams.
They will reduce the breach risk and help improve security posture more effectively and efficiently. It means that machine learning is very important to cyber security. It is a sub-field of Artificial Intelligence.
Cyber security systems can analyze patterns. Machine learning also helps to make your company safe from cyber-attacks.
Moreover, it is about building patterns and manipulating them with algorithms. Usually, machine learning depends on some patterns that can make new predictions.
It will depend on the new data, such as the shopping app offering several recommendations focused on your prior views.
How is Machine Learning Used in Cyber Security?
Machine learning makes assumptions about the behavior of computers. However, these algorithms depend on previous datasets and statistical analysis.
After that, the computer adjusts its action, even performing functions it wasn’t programmed to do. These capabilities make machine learning an important cyber security asset.
1. Uncover Network Vulnerabilities
Instead of waiting for cyber-attack to happen, companies take a more proactive approach with machine learning.
Penetrating testing includes simulating a cyber-attack to determine the weak points in the company’s networks, firewalls, and systems.
Machine learning performs this task and applies software patches, code fixes, and many other solutions to deal with the security site of an organization.
Additionally, the ability to learn machine learning in cyber security from historical data let it choose unusual software and user behavior during these kinds of training sessions.
After that, the technology remembers how a particular cyber-attack happens. In addition to this, it also finds out which poses the highest threats depending on the network’s vulnerabilities.
2. Find Out Threats in Early Stages
At the same time, with the ability to categorize via millions of files and recognize potentially hazardous ones, machine learning cyber security is increasingly used to uncover threats.
So it can squash them before they can wreak havoc. For example, in 2018, Microsoft software showcased this skill when cyber crooks tried to contaminate over 400,000 users with a crypto currency miner during a 12-hour time frame.
Window Defender from Microsoft stopped the attack. Keep in mind this software works several layers of machine learning to detect and block perceived threats.
However, the crypto miners were closed almost as soon as they started digging.
3. Email Monitoring
There is no doubt that monitoring employees’ official email accounts are very important in a company. It will keep your company secure from cyber security attacks like phishing.
Remember that phishing attacks can be done by sending fraudulent emails to employees to ask about their private information.
It will include their banking and credit card details, sensitive information about their jobs, company passwords, and much more.
However, machine learning in cyber security software is perfect for avoiding these phishing traps. In addition, it monitors professional emails to check if any features point out a cyber-security threat.
Using natural language processing, companies scan their emails and see if there is anything unusual, like some phrases and patterns pointing out that the email is a phishing attempt.
4. User Behavior Modeling
Some cyber threats can attack a specific company. They steal the login credentials of any of their users. After that, they will illegally log in to the network.
Undoubtedly, it can be very hard to identify them with normal antiviruses. It is because the user credentials are original, and the cyber-attack may even occur without anyone knowing.
Machine learning in cyber security algorithms can help through user behavior modeling. They are trained to recognize each user’s behavior, like their login and log-out patterns.
After that, whenever a user behaves out of their normal behavioral method, the machine learning algorithm can recognize it and alert the cyber security team that something is out of the ordinary.
Some user behavior patterns’ changes are completely natural, but this will still help catch more cyber threats compared to conventional methods.
It is a fact that traditional phishing detection strategies alone lack the accuracy and speed to recognize and differentiate between harmless and malicious URLs.
The latest machine learning in cyber security algorithm predictive ZURL classification models are required to understand the designs.
They can recognize the patterns that tell you about malicious emails. To do this, the models contain features like email headers, punctuation patterns, body data, and more to determine and differentiate the malicious from the harmless.
Companies that are using Machine Learning in Cybersecurity
There are a lot of companies and other organizations that are using and trusting machine learning in their cybersecurity. Some of them are Microsoft, Forcepoint, Blackberry, Splunk, CrowdStrike, and so on.
They are using machine learning to detect threats quickly so that they can take further actions. Even Microsoft has developed its own platform for cybersecurity which is known as ATP (Windows Defender Advanced Threat Protection).
In several ways, machine learning supports modern cybersecurity solutions. Individually, each machine learning cyber security solution is valuable.
Together they can change the game and maintain a very strong security posture in a dynamic threat landscape. With the help of machine learning, companies can rapidly detect known bad behaviors is the best use case for security.
And when talking about the future of machine learning in cybersecurity, it is really bright. Upcoming trends in the cybersecurity landscape are enhancing it and making it more vital and better than before. As we know, in the future, challenges in cybersecurity are going to increase in a big manner. And only machine learning can address that.
However, after first profiling devices and knowing regular activities, machine learning cyber security is well aware of what is normal and what is not—with apps and data in several different locations, being able to determine the trends across a large volume of devices. In addition to this, machine learning can do what humans can’t. It empowers automation for insights at scale.
Read Related: Machine Learning in Credit Card Industry!