Combating Cyber-Threats with Unsupervised Learning

As the world moves into a fully digital age, cyber threats like massive data breaches, hacking into personal and financial data, and any other digital source that can be used to harm people are on the rise. Security experts are using AI more and more to stay one step ahead of these attacks. They use every tool they have, including unsupervised learning methods, to do this.

People think that machine learning in cybersecurity is still in its early stages, but since 2020, there has been a lot of support for adding more AI to fight cyber threats.

Understanding how machine learning can be used in cyber security, recognizing the need for unsupervised learning methods in cyber security, and knowing how to use AI to fight cyber attacks are the keys to fighting cybercrime in the years to come.

Unsupervised Learning

Using Machine Learning to Improve Cyber Security

Cybercrime is scary because it can take up to six months even to find a breach, and it takes about 50 days on average from when a breach is found to when it is reported. That’s a long time to leave yourself open to a cyber attack.

Machine learning can be used to stop cyber attacks before they happen. For example, cybersecurity systems can use machine learning to analyze patterns (even in real-time! ), learning from them to help stop repeat attacks and respond to strange or changing behavior.

Also, it can help cybersecurity teams be more proactive about stopping threats and responding to live attacks instead of trying to put the pieces back together after a breach is found.

Learning methods without being watched can start to pick up on seemingly harmless patterns that aren’t the norm. During the normal tasks that people in cyber security do every day, it can be easy to miss these strange actions. It can help them make better use of their resources by cutting down on their time doing things by hand.

Unsupervised Learning

Types of Unsupervised Learning

When it comes to machine learning models, there are many different ways to train them. There are two ways to learn how to use models: supervised learning and unsupervised learning. We’ll talk about unsupervised learning in this article.

Unsupervised learning techniques is a type of machine learning that does precisely what its name says: users don’t have to watch over the model. Instead, the unsupervised model works by itself to find patterns and data not known before.

In short, unsupervised learning methods for machine learning mean that the AI model is “trained” with little to no human help.

Unsupervised Learning

At first glance, this might not make sense. Don’t you want to be able to teach your machine learning model how to recognize, identify, and report possible cyber-attacks? Yes, but the problem is that there are so many ways a cybercriminal could choose to attack your organization or business that you might unintentionally teach it to ignore other cyber threats.

Unsupervised learning lets AI models figure out things independently in ways that we might miss. More importantly, you don’t have to experience a cyber attack or make up a fake situation for your AI model to learn from!

This means that unsupervised learning methods can predict and protect against future threats without going through a breach or attack like the one they are trying to protect against.

Unsupervised learning includes clustering, representation learning, and density estimation. This lets these models find and group activities that may seem strange, suspicious, or are at least new to the model. This can then alert cybersecurity teams to what the model may think are potential cyber threats.

Traditional methods of cyber defense use data labeling to figure out what kind of threat is there and how to deal with it. This can slow things down, which could be bad for a business and its digital assets.

Unsupervised Learning to Combat Cyber Threats

You should never rely too much on AI for cybersecurity, but unsupervised learning models can help you fight these cyber attacks. Getting started, on the other hand, can be a little scary.

The steps below can help you get your unsupervised machine learning models up and running as soon as possible.

Unsupervised Learning

1. Identify AI Processes

Not every cybersecurity process works well with a machine learning model. For example, a machine learning model trained using an unsupervised learning method wouldn’t be very good at fixing a data breach, but it would be great at spotting the first signs of an attempted cyber-attack.

Review your current cybersecurity strategies and processes carefully to see where AI models can be used and implemented well without taking away from the work of your cybersecurity team.

2. Unsupervised Learning Benchmarks

Before you let your AI model run on its own, you need to set some standards. This will let you know that your machine learning model is helping your cybersecurity efforts and not hurting them.

This also means that you will need to set up a way to check on your AI model to make sure it is using the data correctly. When you know what success looks like for your unsupervised learning methods, you will be able to make the proper course corrections.

3. Monitor & Report

Once your machine learning model has been taught how to fight cyber threats and find cyber attacks, monitoring and reporting will be very important for the success of your cybersecurity efforts.

Methods for learning without being watched can be powerful and valuable, but these models can still misinterpret data.

AI models trained using unsupervised learning methods have been known to group and classify data that have nothing in common, so there needs to be a way to fix these mistakes.

Most of the time, though, it’s essential to keep a close eye on these machine learning models because you never know when a human can start to recognize cyber threats based on the data before the AI model can put the pieces together. AIs still can’t do a lot of what humans can do regarding cybersecurity.

Unsupervised Learning

Conclusion

Cyber attacks are becoming more common, and unsupervised learning methods can help you make machine learning models that your cybersecurity teams can use to stop them.

Is there something you wanted to know more about that wasn’t discussed here? Do you think we forgot to mention something important?

Always happy to hear your thoughts and answer any questions or concerns about AI and cybersecurity.

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