Using Unsupervised Learning to Combat Cyber Threats
Unsupervised learning methods for AI can be used to fight cyber threats before they even happen. Here we cover common methods used in cybersecurity.
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As the world enters a digital age, cyber threats are rising with massive data breaches, hacks into personal and financial data, and any other digital source that people can exploit. To combat these attacks, security experts are increasingly tapping into AI to stay a step ahead, using every tool in their toolbox, including unsupervised learning methods.
Machine learning in the cybersecurity space is still in its infancy stage, but there has been a lot of traction since 2020 to have more AI involved in combating 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 implement AI in combating cyber attacks are the key to fighting cybercrime in the years ahead.
Using Machine Learning in Cybersecurity
The scary thing about cybercrime is that it can take up to six months even to detect a breach, and it takes an average of roughly 50 days, from the time a breach is found to the time it is reported. That is a lot of time to be at the mercy of a cyber attack.
Machine learning in Cybersecurity
Machine learning can be used as a preventative measure to avoid cyber attacks. For example, cybersecurity systems can use machine learning to evaluate patterns (even in real-time!), learning from them to help prevent repeat assaults and respond to strange or changing behaviors.
Furthermore, it can assist cybersecurity teams in being more proactive in preventing threats and responding to live attacks rather than trying to pick up the pieces after the breach is discovered.
Unsupervised learning methods can begin to recognize patterns that may seem benign but are outside of the norm. These unusual behaviors can be easy to overlook in the regular tasks those in cyber security perform every day. It can help firm up their resources more strategically by reducing time spent on manual tasks.
Why You Need Unsupervised Learning Methods in Cybersecurity
There are various ways these models can be trained when it comes to machine learning models. There are supervised learning and unsupervised learning methods for the models, and it is the latter of these two that we will be discussing for this article.
Unsupervised learning is a machine learning technique that does exactly what it's called: users do not need to supervise the model. Instead, the unsupervised model works on its own to discover patterns and information that was previously undetected.
In a nutshell: unsupervised learning methods for machine learning mean there is little-to-no hands-on "training" of the AI model.
On the face of it, this may seem counterintuitive. Don’t you want to be able to train your machine learning model on how to recognize, identify, and report potential 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 may unintentionally train it to ignore other cyber threats.
Unsupervised learning allows the AI models to come to their conclusions in ways we might overlook. But, more importantly, you don’t have to experience a cyber attack or create a false scenario for your AI model to learn from!
This means unsupervised learning methods can forecast and protect against future threats without undergoing a similar breach or attack.
Unsupervised learning involves clustering, representation learning, and density estimation processes. This allows these models to identify and group activities that may seem unusual, suspicious, or are at least unrecognized by the model and alert cybersecurity teams to what it may consider potential cyber threats.
Traditional cyber defense methods rely on data labeling to identify a particular danger and then implement a response. Unfortunately, this can cause a delay that might be dangerous to a business and its digital assets.
How to Implement Unsupervised Learning Methods to Combat Cyber Threats
You never want to become too reliant on AI for your cybersecurity needs, but unsupervised learning models can be an asset to fighting these cyber attacks. Getting started, though, can feel a bit daunting.
Learning Methods in Cybersecurity
The following steps can help you start training and implementing your unsupervised machine learning models on the right foot!
1. Identify Processes Where You Can Implement AI
Not all of the processes in cybersecurity are a good fit for a machine learning model. For example, a machine learning model trained to utilize an unsupervised learning method would not be entirely helpful in correcting a data breach but is excellent when helping catch the early signs of cyber attack attempts.
Carefully review your current cybersecurity strategies and processes to determine where AI models can be used and implemented well without taking away from the work of your cybersecurity team.
2. Establish Success Benchmarks for Your Unsupervised Learning Methods
Some benchmarks will need to be set before letting your AI model run loose. This will help you know that your machine learning model is helping and not hindering your cybersecurity efforts.
This also means that you will need to be creating a process for checking in with your AI model to ensure it is interpreting data correctly. Then, when you know what success looks like for your unsupervised learning methods, you will be able to course-correct appropriately.
3. Monitor & Report
Once your machine learning model is trained to combat cyber threats and detect cyberattacks, monitoring and reporting will be critical steps for the success of your cybersecurity efforts.
Unsupervised learning methods can be powerful and effective, but these models can still interpret data incorrectly.
AI models that have been trained using unsupervised learning methods have been known to categorize and cluster data that, in all actuality, has nothing in common, so there has to be a process for correcting these interpretations as they arise.
For the most part, though, it is essential to keep a close eye on these machine learning models simply because you never know when a human can begin to recognize cyber threats based on the data before the AI model can put the pieces together. Human intuition is still a massive part of cybersecurity that an AI cannot replicate.
Want to Learn More About AI and Cybersecurity?
Cyber threats are on the rise, and unsupervised learning methods can help you create machine learning models for your cybersecurity teams that can combat cyber attacks.
Is there anything we didn’t cover here that you wanted to know more about? Is there some critical information you think we overlooked? We would love to hear your feedback and answer any questions or concerns you have about AI and cybersecurity. Just comment below or contact us to answer any questions.
Published at DZone with permission of Kevin Vu. See the original article here.
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