Big data and machine learning: The perfect ingredients for cybersecurity


Cyber threats are continually evolving today, making the big data and machine learning parts of your cybersecurity strategy even more necessary than in the past. Undoubtedly these threats will continue evolving these parts of your strategy will also continue to grow in importance. In fact, we’re already seeing companies combine them together to form an even stronger approach to cybersecurity.

When it comes to market size, about US$800 million was spent on applying these technologies to security in 2016. Today, this number has grown as it’s now common to use them to analyze data so businesses can uncover hidden patterns and detect any threats.

Keeping Pace with Hackers

Protecting your business against hackers who are developing increasingly sophisticated strategies to bypass your security is growing even more challenging today. They’re also using machine learning on their side to:

  • Automate their attacks in a way that makes their breaches even harder to detect
  • Automate the victim selection process so they can tell who’s most vulnerable to their threats
  • Find weak points in your cyber defense system
  • Develop new ways to bypass your security software

This has created a never-ending battle between hackers and your defense systems that are growing more complex as AI fights against itself. To stay ahead of this “game” Inside Big Data says cyber defense systems must deploy machine learning algorithms that are at least as powerful and complex as what hackers are using, but preferably even stronger.

Using Big Data and Machine Learning Together

Big data is the most important ingredient when it comes to machine learning. This is because sensitive data is exposed so hackers may steal it. Companies must employ network security intelligence to detect attacks and aggregate information (e.g. directories, URLs, parameters, acceptable user inputs).

On the other hand, machine learning is important because it’ll analyze the information for you. As it goes through this massive amount of information it’ll find patterns, correlations, and anomalies. Once this process is complete, you’ll have something your security team can easily read and understand.

Remember, simply detecting that a security event exists isn’t useful. Your security team must be able to understand it so they can access the threat and focus on those that aren’t false positives. With machine learning-driven analysis attacks won’t go unnoticed.

big data and machine learning
Your security team must be able to understand it so they can access the threat and focus on those that aren’t false positives.
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The Importance of Natural Language Processing Capabilities

Unfortunately, a lot of information regarding security events isn’t immediately apparent because it comes in the form of unstructured text that’s distributed across millions of websites – sometimes it’s even buried in the dark web. Interpreting this information is impossible for humans but it’s something that big data analytics together with machine learning can accomplish for you.

When your platform has natural language processing capabilities you can find unstructured text and gather any relevant data. You can then use machine learning to make sense of this text, regardless of things like language, punctuation, format, or jargon. Once this is done your security professionals will have something that’s readable and that they can use.

How Big Data and Machine Learning Work with the Human Mind

You can think of big data and machine learning as being part of the same architecture. They work together in a powerful way to protect you from most complex threats. This is why you need to include it when creating a strong platform. You’ll want to make sure that you have an inbuilt data management platform that collects and organizes big data then uses machine learning algorithms to analyze the data so it can respond to threats and prevent any new attacks from occurring.

Without such a system in place, your security team couldn’t gather and organize a lot of information and know what’s going on. Of course, security professionals will always play an important role here as it’s up to them to decide how they’ll react, but it’s up to machine learning to distill the large amounts of data into information they can act on. Machine learning simply makes their job faster and easier.

Why Big Data is Better

Clearly, you can see that big data and machine learning must be used together in your cybersecurity plan. Threat data has the information your cybersecurity team needs so it can work effectively. When you have a large threat dataset machine learning can spot even more threats and variants so you can decide what the best way of mitigating them is before they infect your system. The more information that’s available, the better your threat intelligence will be at defending you.

Fortunately, it isn’t difficult to collect and process the big data you need to analyze. However, this process can be rendered ineffective if you get “dirty data” – that which is incomplete or contains errors. When this happens, you must use data cleansing or wrangling before analyzing it. Unfortunately, this is a very labor-intensive process, requiring about 50% – 80% of a data scientist’s time and costing about $3.1 trillion per year in the U.S. alone.

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What’s Being Done About the Threat Data Fact

Trend Micro is using machine learning to focus on the quality and quantity of datasets that are collected and analyzed. They have years of security research experience that provides them with huge amounts of threat and malware data that’s accurately labeled. They’re also continuing to accurately understand and label new data. Their focus throughout this process is to make sure everything is of high quality so they can optimize the machine learning systems’ performance.

A great example of how they do this is seen in what they do for support vector machines (SVM) for email. For machine learning technology to work properly here and identify spam vs. legitimate emails they must be properly trained. This is done by creating a carefully vetted dataset against which to test everything. There wasn’t even any duplicate data included because this could skew the data in a way that’d cause false negatives and false positives. These datasets must also represent the current email landscape, containing samples from a variety of relevant sources.

What are your thoughts? Let us know in the comments below or on Twitter, or Facebook. You can also comment on our MeWe page by joining the MeWe social network.

Last Updated on February 3, 2021.


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