K-Means Clustering and UseCases in Security Domain.

K means is one of the most popular Unsupervised Machine Learning Algorithms Used for Solving Classification Problems. K Means segregates the unlabeled data into various groups, called clusters, based on having similar features, common patterns.

What is clustering?

Clustering is one of the most common exploratory data analysis techniques used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different.

Unlike supervised learning, clustering is considered an unsupervised learning method since we don’t have the ground truth to compare the output of the clustering algorithm to the true labels to evaluate its performance. We only want to try to investigate the structure of the data by grouping the data points into distinct subgroups.

What Is K-Means Algorithm?

K-means Algorithm is an Iterative algorithm that divides a group of n datasets into k subgroups /clusters based on the similarity and their mean distance from the centroid of that particular subgroup/ formed. Or K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.

The term ‘K’ is a number. You need to tell the system how many clusters you need to create. For example, K = 2 refers to two clusters, K=3 refers to three clusters so on. There is a way of finding out what is the best or optimum value of K for a given data.

For a better understanding of k-means, let’s take an example from cricket. Imagine you received data on a lot of cricket players from all over the world, which gives information on the runs scored by the player and the wickets taken by them in the last ten matches. Based on this information, we need to group the data into two clusters, namely batsmen and bowlers.

Types of Clustering:

The various types of clustering are:

• Hierarchical clustering
• Partitioning clustering

Hierarchical clustering is further subdivided into:

• Agglomerative clustering
• Divisive clustering

Partitioning clustering is further subdivided into:

• K-Means clustering
• Fuzzy C-Means clustering

Use-Cases in the Security Domain

We utilize K-means clustering in many areas, and we have numerous use-cases in the security sector. One of the most significant topics where we use k-mean clustering for an optimal method is here.

Using K-Means Clustering Algorithm to Analyze Logs from Proxy Server and Captive Portal:

The amount of data created by users’ different interactions with websites is continuously rising, as is the amount of traffic on the World Wide Web. As a result, online data becomes one of the most important tools for retrieving information and discovering new knowledge.

Web Usage Mining was used to find valuable and fascinating patterns from the web data using logs from the Proxy Server and Captive Portal databases. In addition, the k-means clustering method was utilized to create particular groupings of user access patterns based on the number of user sessions and websites visited by network users. It was discovered as a result of the findings

Cyber-Profiling Criminals:

Cyber-Profiling is the process of collecting data from individuals and groups to identify significant correlations. the idea of cyber profiling is derived from criminal profiles, which provide information on the investigation division to classify the types of criminals who were at the crime scene.

Call record detail analysis:

A Call detail record (CDR) is the information captured by telecom companies during the call, SMS, and internet activity of a customer. this information provides greater insights about the customer’s needs when used with customer demographics. in?this article, you will understand how you can cluster customer activities for 24 hours by using the unsupervised k-means clustering algorithm. it is used to understand segments of customers with respect to their usage by hours.

Anomaly detection:

Anomaly detection refers to methods that provide warnings of unusual behaviors which may compromise the security and performance of communication networks. Anomalous behaviors can be identified by comparing the distance between real data and cluster centroids. Identifying network anomalies is essential for communication networks of enterprises or institutions. The goal is to provide an early warning about an unusual behavior that can affect the security and the performance of a network.

Malware Detection:

The process of identifying the existence of malware on a host system or determining whether a certain application is dangerous or benign is known as malware detection. Malware detection techniques are essential for identifying malware attacks that have a significant influence on the cyber world. Unsupervised machine learning may detect malware attacks by detecting the behavior via clustering.

Insurance fraud detection:

Machine Learning plays an important role in fraud detection and has a wide range of applications in the automotive, healthcare, and insurance industries. It is feasible to identify new claims based on their closeness to clusters that suggest fraudulent tendencies using previous data on fraudulent claims. Because insurance fraud has the potential to cost a firm million of dollars, the ability to identify fraud is critical.

K-Means Clustering is one of the most common algorithm used for clustering.

Hope you found it informative.

Thank You.