One of the biggest problems of the modern age is data processing. This problem is also very important for the private sector. Few companies remain today that have not automated their data processing systems. In addition, most companies carry out automation comprehensively. They combine data from many different sources to make the best possible defense. These resources include public websites, the dark web, and technical resources. The data collected plays a key role in cyber defense, so experts recommend using machines to optimize data collection. So how can you improve your threat intelligence with machine learning?
How to Improve Your Threat Intelligence with Machine Learning?
Experts recommend four different machine learning methods for this. The first is to categorize the data, the second is to analyze the data in many languages at the same time, the third is to determine the risk levels, and the last is to make different models according to different scenarios.
1. By categorizing the data:
One of the most important steps in data processing is how you parse and categorize topics. This decomposition is usually done according to names, attributes, and relationships. This allows the data to be sorted. In addition, thanks to machine learning, data can be categorized independently of their language, even if they are in different languages. In other words, words or sentences written in different languages but mean the same are in the same category.
Categorizing concepts and events in this way makes the job of cybersecurity experts much easier. Because they don’t have to sort through complex data on their own, and when they need to do a broader review, they can do it without any difficulty.
2. By reviewing multiple languages simultaneously:
Since machine learning has natural language processing, it can examine different languages beyond keywords. In this way, it can process data in different languages and create a meaningful database. Also, thanks to machine learning, you can let go of the unnecessary data and examine the primary content directly. In addition, you can categorize this content by naming it as text, prose, data logs or code. In this way, you will prevent ambiguity that may occur between concepts.
Normally, these are tasks performed daily by expert analysts. Machine learning prevents these expert analysts from wasting their time on small, automated tasks. It allows you to save time and work more efficiently.
3. By determining the risk levels:
One of the most important benefits of using machine learning in threat intelligence is the ability to rank assets and events by their importance. This way, you can identify risk levels and prioritize more risky situations. For example, you can prioritize malware or potential phishing cases that are urgent.
Prioritization can be done in different ways. In the first, cybersecurity experts identify risks by considering rules based on experience and instinct. The other is done using machine learning. The machine considers a dataset it has already viewed and prioritizes accordingly. Accordingly, the machine labels events according to whether they are critical or not, that is, events have both a risk score and a label describing the risk.
When you automate prioritization, it becomes very easy to sort out false positives and determine what to prioritize. According to research, companies that use machines to identify risks have developed a more effective defense system than companies that do not use them in the past year.
4. By using modeling based on possible scenarios:
Machine learning always stores past events and data in its system. So, it can analyze where threats may come from using this vast data network that it previously obtained and categorized. It can model that it has a deep data pool better than even expert cybersecurity professionals. In this way, machines can predict the future very close to reality.
In recent years, it is on everyone’s lips to take advantage of “Big Data”. This is exactly what we’re talking about here. Machine learning provides countless benefits to people in the use of “Big Data”. These models based on possible scenarios become more accurate as our data pool expands.
How Our Threat Intelligence Leverages Machine Learning
- Hackers share millions of emails, passwords, and usernames on the Internet every day. The Personally Identifiable Information (PII) Tracking engine in our threat intelligence tool constantly examines these violations. If it detects any data that could affect your company, it will notify you quickly.
- In cybersecurity defense, it is very important to constantly scan for vulnerabilities. This can place additional strain on any system. To avoid overloading the client system, our Threat Intelligence tool has a Passive Vulnerability Scan Engine. This engine handles the current state of the target system and periodically checks whether the system is vulnerable to a newly vulnerability.