K-means clustering and its Real World use cases in the Security Domain

Clustering

Clustering is used to get an intuition about the structure of the data. It defined as the task of identifying subgroups in the data such that data points in the same 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 investigate the structure of the data by grouping the data points into distinct subgroups.

K-means clustering

K-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. In K-Means, each cluster is associated with a centroid.

The main objective of the K-Means algorithm is to minimize the sum of distances between the points and their respective cluster centroid.

K-means algorithm is an iterative algorithm that tries to partition the dataset into?K pre-defined distinct non-overlapping clusters where each data point belongs to only one group. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different as possible.

It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster centroid is at the minimum. The less variation we have within clusters, the more homogeneous the data points are within the same cluster.

K-means algorithm works as follows:

• Specify the number of clusters?K.
• Initialize centroids by first shuffling the dataset and then randomly selecting?K?data points for the centroids without replacement.
• Keep iterating until there is no change to the centroids. i.e assignments of data points to clusters aren’t changing.
• Compute the sum of the squared distance between data points and all centroids.
• Assign each data point to the closest cluster.

Use Cases in the Security Domain:

Here is a list of some of the interesting use cases of K-means in the Security Domain:

Customer segmentation

Clustering helps marketers improve their customer base, work on target areas, and segment customers based on purchase history, interests, or activity monitoring. how telecom providers can cluster pre-paid customers to identify patterns in terms of money spent in recharging, sending SMS, and browsing the internet. the classification would help the company target specific clusters of customers for specific campaigns.

Identifying crime localities

With data related to crimes available in specific localities in a city, the category of crime, the area of the crime, and the association between the two can give quality insight into crime-prone areas within a city or a locality.

Insurance fraud detection

Machine Learning has a critical role to play in fraud detection and has numerous applications in automobile, healthcare, and insurance fraud detection. utilizing past historical data on fraudulent claims, it is possible to isolate new claims based on their proximity to clusters that indicate fraudulent patterns. Since insurance fraud can potentially have a multi-million dollar impact on a company, the ability to detect frauds is crucial.

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. We 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.

Automatic clustering of it alerts

Large enterprise infrastructure technology components such as network, storage, or database generate large volumes of alert messages. Because alert messages potentially point to operational issues, they must be manually screened for prioritization for downstream processes. Clustering of data can provide insight into categories of alerts and mean time to repair, and help in failure predictions.

Rideshare data analysis

The publicly available uber ride information dataset provides a large amount of valuable data around traffic, transit time, peak pickup localities, and more. Analyzing this data is useful not just in the context of uber but also in providing insight into urban traffic patterns and helping us plan for the cities of the future.

Crime document classification

Cluster documents in multiple categories based on tags, topics, and the content of the document. This is a very standard classification problem and k-means is a highly suitable algorithm for this purpose. The initial processing of the documents is needed to represent each document as a vector and uses term frequency to identify commonly used terms that help classify the document. the document vectors are then clustered to help identify the similarity in document groups.

These were few use cases but the list goes on be it in Security Domain or any other, K-means is a very effective as well as an easy way of Clustering in machine learning.

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