K-means clustering and its use cases in Security Domain

Hello friends, hope you all are fantastic!!

In this article, I would like to tell you all about what is clustering, K-means and how K-means helps to prevent many cyber attacks. So let's get started.

What is clustering in Machine Learning?

What is K-means Clustering?

So finally you can relate the explanation with the following diagram:

How K-means clustering helps in preventing cyber-attacks?

So, as you can see that the information of all the users that have tried connecting to our website is shown inside the log file. So if we try to form groups based on the data of each user that connected to our website then we have to choose some data using which different groups could be formed. For eg, we could choose the error code that the user got while trying to access our webpage. So from the image, we can group the data together in one cluster which has 404 error, and group another data together in another cluster which has 544 error code and so on as shown below:

This clustering or forming of clusters is done automatically by the “K-means” algorithm and so this helps the cybersecurity experts or the SecOps teams of any organization to analyze the clusters and the common pattern that is repeated again and again that might be a security threat and so this also helps them to do Root Cause Analysis. So, this is the way how K-means algorithm helps in preventing cyber attacks by hackers. Some of the common attacks against which K-means clustering is used are DoS, Probe, U2R, R2L, etc.

Some more use cases of K-means Clustering in Security World

Intrusion Detection System (IDS)

Spam Filtering

K-means Clustering is a successful method of distinguishing spam. The way that it works is by taking a look at the various areas of the email (header, sender, and content). The information is then gathered together. These gatherings would then be able to be arranged to recognize which are spam. Including clustering in the classification process improves the accuracy of the filter to 97%.

Like these, there are many more use cases of K-means Clustering in the Security World. Thanks for reading:)

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