Using Machine Learning For Better Threat Intelligence

December 16, 2021
3 min read time
Using Machine Learning For Better Threat Intelligence

Organizations are operating digital infrastructure at a scale never seen before. This scale is producing enormous volumes of threat data from multiple sources, which requires processing across millions of lines of input on a daily basis. Many businesses and security teams are looking to machine learning (ML) to try to make sense of all the data. In particular, if ML technology can help them to identify threats and respond to them faster and more accurately than a team of human experts doing the work manually, it would provide a significant upgrade to cybersecurity. 

In this article, we look at the different ways that a machine learning engine, such as the one built into Snapt’s NovaSense threat intelligence platform, can help security teams to make critical decisions.

1. Structure data into entities and events

One of the most difficult things for any human to do when trying to make sense of vast amounts of data is to effectively structure them into various categories to help make sense of what needs attention and what doesn’t. This is something that ML, artificial intelligence (AI), and the science of ontology can help to solve because machines are able to rapidly scan and sort through data to provide the needed output. 

Ontology looks at how concepts can be split up and grouped together. In data science, ontologies represent categories of entities based on their names, properties, and relationships to each other, making them easier to sort into hierarchies of sets. 

Ontologies and events enable powerful searches over categories, letting analysts focus on the bigger picture rather than having to manually sort through data, drastically reducing the time it takes to get results from the data.

For example, NovaSense quickly classifies the ingested threat data to identify threats like malware, cybercrime, platform abuse, and more.

2. Structure text in multiple languages through natural language processing

Natural Language Processing (NLP) is one of the most developed forms of machine learning that we can see in regular use by applications that can translate text from one language into another. This is something that makes it far easier to communicate across different languages (even if not yet perfectly).

However, NLP is used only for translation but also for security protection as AI systems now have the ability to scan communications, data, and other aspects of code in different languages to identify potential threats. This helps to detect threats that would have gone completely undetected only a few years ago because threat searches targeted only the most common languages. 

A threat intelligence system like NovaSense using NLP to identify threats in any language can alert security teams to threats that might affect them from the other side of the globe, in languages they don’t natively understand and allow them to be better prepared in the global battle for data security. 

3. Classify events and entities, helping human analysts prioritize alerts

Security teams can determine where to focus their efforts by analyzing data, identifying risks, and prioritizing different threats or events by severity and probability. However, this is a time-consuming and high-skill process.

An ML engine can replace this manual process by learning how to identify patterns of interest to security teams, and which events or potential threats are likely to target the user’s organization and have the biggest impact. This analysis connected to an observability solution can ensure these events are highly visible and easily understood.

For example, NovaSense provides a predictive scoring system to help teams triage threats, rather than overwhelming them with lots of alerts that do not represent a genuine security risk or require human attention. 

By using ML to help focus on events that are most likely to be problematic, security teams can save countless hours spent paying attention to things that aren’t important.

4. Forecast events and entity properties through predictive models

It’s not all about interpreting data and focusing on what’s urgent right now. By applying AI to the deep pools of data previously mined and categorized by the ML engine, a threat intelligence platform can effectively predict future threats and provide security teams with recommendations for pre-emptive defense. 

Apart from security teams using this analysis to plan for future threats, software architects can use it to design applications that will be resilient to security threats most likely to emerge in the near future, before they happen.

You need ML incorporated into your threat intelligence for success

There is simply just too much threat data for security teams to rely on human effort to keep their systems safe. 

With security threats only growing in complexity and the exposure to risk higher than ever, businesses need to look at ways of identifying and blocking security threats pre-emptively instead of only shutting them down when they occur. 

Machine learning capabilities provide this opportunity through their ability to analyze voluminous amounts of data quickly and accurately and provide a level of pattern recognition, triage, and predictive analysis that would take much longer (if possible at all) even with skilled human operators.

This is why solutions like the NovaSense threat intelligence platform and its built-in ML engine and AI assistance are so critical to the backbone of any modern security posture. 

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