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The problem of cybersecurity is on the front burner of thousands of companies worldwide. Cybersecurity threats and attacks constantly evolve. Hackers try to get to our computers and companies in order to steal money, obtain valuable data, and even use their computing power (e.g., to mine cryptocurrencies). And since the bad guys don’t ever stop their attempts to hack into yet another company, we need a shield designed for the challenges of the 21st century. And we believe machine learning in cybersecurity has got whatever it takes to become this shield.
When we talk about a specific technology or its specific use, we usually shortly describe the big idea behind it. In many instances, at least when it comes to AI-related technologies and solutions, this big idea is to make things more effective, automated, improved. With machine learning consulting in cybersecurity, it’s no different. The main purpose is to make cybersecurity much, much more effective. And that’s a good thing since the stakes are high. So, what do we need to know about ML in cybersecurity? And is this revolution already happening? Let’s find out!
Times when Trojans were the most popular type of malicious software are long gone. Today, cyberattacks are common and advanced.
There are several types of cyberattacks that are most common, though, and these are:
Of course, all of the attacks mentioned above are typically code-based. However, there are also threats that require something more than just malicious code or app. Hackers frequently use social engineering to get what they want. In many instances, they try to convince someone to give up the necessary information. And sadly enough, they frequently succeed.
The next vital threat is related to your employees and contractors. You have to acknowledge that insider attacks happen, and many companies experienced them when employees or subcontractors tried to steal valuable business data.
Moreover, we have to keep in mind that many modern IT tools are open-source. And that’s both a good thing and a bad thing. Good, because you can use these tools without any costs or limitations. On the other hand, open-source tools don’t always have a developed security layer, which means they are prone to attacks. This also refers to open-source CMS platforms, like, for example, WordPress.
As you can see, there are quite a lot of different threats and attacks your company ought to defend against. Thankfully, there are many ways to do so. Let’s take a closer look at the most effective ones.
When it comes to cybersecurity, you have to be protected on all fronts. This means that you should opt for a set of solutions that will protect you against every possible attack. This includes several common solutions:
A robust password manager can help generate and securely store your passwords.
As you know from our other blog posts, machine learning is a subset of AI allowing computers and machines to learn themselves and improve their operation over time. The question is, how can this be used in cybersecurity? For starters, intelligent algorithms have the ability to search through millions of files and identify potentially malicious ones. This means that threats can be discovered much quicker before they become really dangerous.
In general, the whole idea of machine learning in cybersecurity is simple. Every year, we have more and more data available. We can analyze it much more effectively with machine learning algorithms and use it to devise new defense mechanisms. That’s why ML-based antiviruses can quickly discern any potentially hazardous activity–they are simply looking for anything that differs from everyday use.
The vast majority of machine learning techniques can be used to improve cybersecurity programs and solutions. For example:
However, you have to understand that all of the aforementioned techniques are usually just a part of the larger cybersecurity infrastructure, not a standalone solution.
You may find it interesting – Machine Learning Techniques – Which one is the best for your project?
In early 2018, there was a high-profile case of a malicious cryptocurrency mining code that hackers used to attack 400,000 computers within just 12 hours. The attack was thankfully stopped thanks to Microsoft Defender–antivirus software development that uses machine learning.
The next example we want to tell you about is the American company Darktrace. Their product, Cyber AI, uses unsupervised machine learning techniques to protect their clients against diverse types of cyberattacks. As the company claims, although rule and signature-based solutions offer some protection against pre-identified threats, the reality is that attacks consistently evade these tools. That’s why Darktrace had to come up with a more advanced and smart solution.
Today, their so-called Darktrace Immune System is the world’s leading autonomous cyber-defense platform. Thanks to machine learning, their systems continually learn how normal users behave, so they can immediately spot potentially dangerous anomalies and deviations.
Read more about Machine Learning Software Tools
Kaspersky is one of the leading antivirus and online security companies. They also use machine learning in their cybersecurity systems. Kaspersky uses both supervised and unsupervised machine learning, as well as deep learning in order to achieve three major objectives:
And these are ML-based techniques they use to achieve them:
Now, let’s take one step further and see how deep learning, a more advanced version of machine learning, can be used in cybersecurity.
Although, in general, deep learning works similarly to ML, it’s far more complex, operates based on a neural network (or even networks), and doesn’t require extensive initial training. So, how can this technology be used in cybersecurity?
Let’s take a look at some possible applications:
You may also find it interesting – Machine Learning and Deep Learning – Comparison
So far, we’ve told you about three such companies. However, let’s dig a bit deeper, because some of these companies have some really amazing projects going on concerning machine learning in cybersecurity. Here we go:
As you already know, Microsoft Defender uses machine learning features to be more effective. However, there’s more, as Microsoft has a whole product called Microsoft Defender Advanced Threat Protection (ATP). In short, ATP is a preventative and post-detection, investigative response feature to Windows Defender, and it comes with several interesting capabilities:
Chronicle offers a threat detection solution built on the power of Google’s infrastructure to help enterprises identify threats at unprecedented speed and scale. Chronicle analyzes large amounts of security data and uses machine learning to condense it into more easily digestible insights.
It’s an American company founded by ex-NSA employees in 2012. Today, SQRRL produces software for big data analytics and cybersecurity. The company helps analyze a variety of sources to track and understand security threats quickly using machine learning. In 2018, SQRRL became a part of the Amazon Web Services family[4].
Naturally, the list of companies using machine learning in cybersecurity is much longer and will extend over time. This technology has everything it takes to become the next game-changer in the cybersecurity sector. Of course, it’s not a flawless solution, and hackers are not going to lower their guard. But frankly, today, it’s the best solution we have to deal with diverse cyber threats. Undoubtedly, in the near future, this technology will grow and more frequently base not just on machine learning but also on deep learning.
Addepto is an advanced AI consulting company. Every day we work with machine learning and deep learning algorithms to up our clients’ game. If you’re looking for a trusted partner who can help you with AI-related challenges, also in cybersecurity, we are at your service. Just drop us a line and show us your project or idea. Surely we will find a way to build something great together. With our help, you have the entire AI world at your service!
[1] Kaspersky.com. Machine Learning in Cybersecurity. URL: https://www.kaspersky.com/enterprise-security/wiki-section/products/machine-learning-in-cybersecurity. Accessed June 10 2021.
[2] TechTarget SearchSecurity.com. Windows Defender Advanced Threat Protection (ATP). URL: https://searchsecurity.techtarget.com/definition/Windows-Defender-Advanced-Threat-Protection-ATP. Acessed June 10, 2021.
[3] Google Cloud.com. Modern detection for modern threats: Changing the game on today’s threat actors. URL: https://cloud.google.com/blog/products/identity-security/introducing-chronicle-detect-from-google-cloud. Accessed June 10, 2021; BuiltIn.com. Machine Learning Cybersecurity. How it works and companies to know. URL: https://builtin.com/artificial-intelligence/machine-learning-cybersecurity. Accessed June 10, 2021.
[4] TechCrunch.com. AWS beefs up threat detection with Sqrrl acquisition. URL: https://techcrunch.com/2018/01/24/aws-beefs-up-threat-detection-with-sqrrl-acquisition/. Accessed June 10, 2021.
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