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Anomaly detection technology can have a positive impact on your business if used well. Some of you reading this article may be thinking of implementing an anomaly detection system to improve the efficiency of your work. However, I think many people are worried because they don’t know how to introduce it in the first place or how to apply it to their business. In this article, we will explain in detail how to create and introduce an anomaly detection system.
What is anomaly detection?
Anomaly detection is a technology that compares large amounts of accumulated data and detects data that is behaving strangely. There are three methods for anomaly detection: “outlier detection,” “abnormal part detection,” and “change point detection.” Since the ability to detect anomalies when they occur has great value, many anomaly detection systems, ranging from simple to complex, are used in a variety of business situations. So, what kind of programs is anomaly detection used in?
Example of using an anomaly detection program
Below are three examples of how an anomaly detection program can be used. From here, we will explain how to use each of them.
- Fraudulent transaction detection
- Detecting equipment failures and improving operating rates
- Defective product detection
Fraudulent transaction detection
Anomaly detection programs are used in financial transactions to detect fraudulent transactions. Fraudulent transactions refer to malicious transactions such as using someone else’s credit card or market manipulation, and are subject to anomaly detection systems. When detecting anomalies, it is necessary to define what constitutes a fraudulent transaction, and an approach based on statistical data is important.
Detecting equipment failures and improving operating rates
Anomaly detection programs are also used to detect equipment defects and breakdowns in factories. If an equipment malfunction occurs at a factory, it will have a major impact on the daily production plan. Therefore, it is necessary to avoid situations where the service becomes unavailable for a long period of time. Therefore, detecting defects in advance, performing appropriate maintenance, and keeping a close eye on defects will lead to improved equipment operating rates.
Defective product detection
Anomaly detection programs are also used to detect defective products. In detecting defective products, AI is trained to identify non-defective products and identifies non-defective products as defective. Until now, each item had to be checked one by one by skilled workers, making detecting defective products a huge burden. However, if AI automatically determines the condition of the product, the workload on workers will be reduced and they will be able to use their time for other tasks.
How to create an anomaly detection program
Anomaly detection programs are constructed through programming. Here, we will explain how to develop an anomaly detection program using Python.
Python is the most commonly used language for development.
The programming language “Python” is often used for anomaly detection programs. This is because Python can build the “neural network” mechanism used for anomaly detection. Python is a programming language that has a wide range of roles, being used not only for machine learning such as anomaly detection, but also for web development and scraping.
Python has a library for anomaly detection programs.
Python has libraries that can be used in anomaly detection systems. A library is a stylized version of a program with specific functionality that can be quoted. By calling this, you can use specific functions efficiently without having to develop them from scratch. Additionally, libraries provide different functionality depending on the programming language. Python provides many libraries that can be used for AI and machine learning.
Libraries used when creating anomaly detection programs in Python
The following seven Python libraries are used in the anomaly detection program.
- Pandas
- NumPy
- SciPy
- PyOD
- Matplotlib
- PyCaret
- Sklearn
Pandas
Pandas is a library that excels at calculating statistics and formatting data from general spreadsheet calculations. In anomaly detection, it is used to process outliers.
NumPy
NumPy is a library that specializes in array processing capabilities such as multidimensional arrays such as vectors and matrices. In anomaly detection, it can be used when you want to efficiently calculate multidimensional arrays.
SciPy
SciPy, like NumPy, is a library for numerical calculations. In anomaly detection, it is useful when you want to perform analysis with a large amount of data.
PyOD
PyOD is a library used to detect outliers in observational data with large amounts of variation. The methods provided by PyOD can be selected depending on the purpose, such as statistical models and machine learning.
Matplotlib
Matplotlib is a library that graphs analyzed data. Even with complex data, trends and characteristics can be easily understood by graphing. It is effective when you want to graph anomaly detection because it can handle various types of graphs and you can select the display format.
PyCaret
PyCaret is a library that provides many algorithms for anomaly detection, and can be used for both supervised and unsupervised learning for anomaly detection. Therefore, it is an effective library when you want to build an anomaly detection program that utilizes machine learning.
Sklearn
Sklearn (scikit-learn) is a library for Python that allows you to use various machine learning methods such as classification, regression, and clustering. It has the feature of being easy to use with other libraries, as it can use the output data of NumPy and SciPy mentioned above as input data.
Process when implementing an anomaly detection program
The process when implementing an anomaly detection program includes the following. In the first stage of project planning and drafting, it is important to clarify the purpose of utilizing an anomaly detection program. If the purpose is not determined, the introduction itself becomes an end in itself, and you may not get the desired effect. In the second stage of PoC, it is essential to repeatedly carry out verification and evaluation. In order to operate smoothly, it is necessary to clarify the indicators for evaluation in advance. In the final stage of application to business, smooth application is required without affecting existing operations. Therefore, it is important to review it regularly during operation.
- Project planning and drafting
- Demonstration of PoC
- Application to business
Use AI and machine learning for efficient anomaly detection
Anomaly detection systems can be made more efficient by using AI and machine learning. With the introduction of AI, patterns and regularities can be calculated from large amounts of data, allowing us to derive what is normal behavior and what is abnormal. As a result, it becomes possible to detect spam such as unsolicited email and fraudulent credit card use.
summary
Anomaly detection is a technology that we would like to proactively introduce as it leads to increased operational efficiency. When implementing it in a company, you can develop a system by using a library that specializes in anomaly detection.
