ANOMALY DETECTION SYSTEM | | Client Case Studies

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ANOMALY DETECTION SYSTEM

The system has many applications in business, from intrusion detection (identifying strange distortions in network traffic that could probe an attack or hack) on the system or health monitoring (spotting a malignant tumor in an MRI scan), and from fraud detection in credit card transactions to fault detection in operating environments.

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Technologies Used

  • PyCharm
  • PyGTK
  • PyQt
  • Python
  • wxPython

Key Challenges

The client needs to develop a system which can provide a machine learning solution to detect anomalies present in the data set of various businesses. However, the provided business challenge can be described as, there are some Network Traffic data, containing multiple data anomalies. These Data anomalies are responsible for creating various problems for the business units to identify the nature and trends of the data and not able to construct any decision/prediction model over the anomaly influenced data set.

The data needs to be adequately sanitized to make it usable for decision-oriented applications. Our team at ValueCoders use the Gaussian Distribution model, which plots the probability values of time-stamp attribute. The mean and co-variance of time-stamps are passed to the Gaussian distribution, which calculates the best possible f1_score and epsilon. These values are computed using the cross-validation data set. Finally, the probabilities of the test data set, which falls below the epsilon value can be marked as outliers.

ValueCoders was approached by the client to develop this application. The company worked closely with the client-consultant to create such an app built using artificial intelligence. Anomalies presented in the data sets are one of the challenges faced by most of the business units and businesses operating without real-time automated anomaly detection, typically rely on dashboards to reveal issues and insights contained in the data.

However, business is all about dealing with constant and variable challenges. Constants challenges are something which is structured in nature and also standardization of the process can help us in dealing with the same.

When they hired ValueCoders, we had to address the following tasks:

  • To develop a system which can deal with variable challenges and patterns which are difficult to identify for the same. Machine learning-based solutions were required by the client.
  • Client-side should be focusing on various challenges and demands faced by businesses today.
  • Machine Learning solutions allows the businesses to have insights into these issues in real-time and reduce the dependency of the offline/Periodic dashboards.

While developing, we faced various challenges, including the following ones:

  • It was challenging to develop a system for businesses which are dealing with the constant and variable challenges where constant challenges are something which is structured in nature and also standardization of the process can help us in dealing with the same.
  • Anomalies presented in the data sets are one of the challenges faced by most of the business units. To develop a system which can detect these anomalies was another major challenge among us.
  • To develop a system which can uncover essential insights in even the most obscure and easily overlooked corners of any data set when it comes to data present in digital businesses.

Our developers at ValueCoders had overcome these challenges with their innovative ideas and technical expertise.

Our Solutions

The idea of developing such an app was the result of long discussions on the customer side, focusing on the various challenges and demands faced by various businesses. They wanted an application that can help them in dealing with some variable challenges and patterns are difficult to identify for the same, in such condition machine learning-based solutions are available in order to provide an insight to the problem.

ValueCoders team accepted the challenge of the complexity of work and started their effort on this anomalies detection system. Few discussions were held among the developer’s team and as a result, planned to build this system.

Below are the steps to identify the anomalies through Machine Learning approach.’

Step 1:- Read the CSV file dataset from which anomalies have to be detected.

Step 2:- Calculate the mean and covariance matrix of the training samples.

Step 3:- Find the Gaussian distribution of the dataset by plotting the random samples according to the mean and covariance matrix.

Step 4:- Calculate the step-size (it denotes the shifting towards the optimum global value in the Gaussian distribution plot in each sample.). Calculate f1_score and minimum epsilon for each value of epsilon with maximum and minimum probabilities according to the step size.

Step 5:- Compare the probabilities of the test dataset with the epsilon. The one which falls below the epsilon could be considered an anomaly.

As a result, it came out as the robust and efficient machine learning solution which can easily detect anomalies present in the data sets.

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