K-means Clustering and its real use-case in the Security Domain


Clustering analysis can be done on the basis of features where we try to find subgroups of samples based on features or on the basis of samples where we try to find subgroups of features based on samples. We’ll cover here clustering based on features. Clustering is used in market segmentation; where we try to find customers that are similar to each other whether in terms of behaviors or attributes, image segmentation/compression; where we try to group similar regions together, document clustering based on topics, etc.

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.

Clustering is used to create a group (cluster) of the data so that it can easily find the necessary data. Clustering is a classification of similar objects into several different groups, it is usually applied in the analysis of statistical data which can be utilized in various fields, for example, machine learning, data mining, pattern recognition, image analysis and bioinformatics

Clustering including supervised learning types. There are four types of clustering algorithms that have been compared based on performance, such as K-Means, hierarchical clustering, self-organization map (SOM) and expectation maximization (EM Clustering). Based on these test results can be concluded that the k-means algorithm performance and EM better than a hierarchical clustering algorithm. In general, partitioning algorithms such as K-Means and EM highly recommended for use in large-size data. This is different from a hierarchical clustering algorithm that has good performance when they are used in small size data

In this post, we will cover only Kmeans which is considered as one of the most used clustering algorithms due to its simplicity.

K-means Algorithm

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

The way k-means algorithm works is as follows:

  1. Specify number of clusters K.
  2. Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement.
  3. Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn’t changing.
  • Compute the sum of the squared distance between data points and all centroids.
  • Assign each data point to the closest cluster (centroid).
  • Compute the centroids for the clusters by taking the average of the all data points that belong to each cluster.

The approach k-means follows to solve the problem is called Expectation-Maximization.

Use-Cases in the Security Domain

Analyzing Logs from Proxy Server and Captive Portal Using K-Means Clustering Algorithm

1. Operational Framework

1.1. Data Collection

1.2. Data Preprocessing

1.3. Data Transformation

1.4. Pattern Discovery

3.5. Pattern Analysis

4. Conclusion

Example : We can use these in the test data in the industry of school Management

Cyber Profiling

Profiling is information about an individual or group of individuals that are accumulated, stored, and used for various purposes, such as by monitoring their behavior through their internet activity .

Cyber Profiling process can be directed to the benefit of:

1. Identification of users of computers that have been used previously.

2.Mapping the subject of family, social life, work, or network-based organizations, including those for whom he/she worked.

3.Provision of information about the user regarding his ability, level of threat, and how vulnerable to threats .

4.Identify the suspected abuser

The way in which Cyber Profiling works :

Use-Cases in the Security Domain

1. 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.

2. 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 its 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.

3. 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.

4. Automatic clustering of it alerts

Large enterprise it 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.

5. 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.

6. 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 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 very effective as well as easy way of Clustering in machine learning.




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