Anomaly detection is identified as one of the most common use cases in Machine Learning. The purpose of finding and identifying outliers is helpful in prevention of fraudulent activities, adversary attacks and network intrusions that have the ability to compromise the company’s future.
The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. Further, it will provide a step-by-step guide on anomaly detection Machine Learning python.
- As of 2021, the market size of Machine Learning was USD 25.58 Billion which is supposed to increase by 35.6% CAGR during 2022-2030.
- By 2028, the market value of global Machine Learning is projected to be $31.36 billion.
- In 2023, the expected reach of the AI market is supposed to reach the $500 billion mark and in 2030 it is supposed to reach $1,597.1 billion.
- 49% of companies in the world that use Machine Learning and AI in their marketing and sales processes apply it to identify the prospects of sales. On the other hand, 48% use ML and AI for gaining insights into the prospects and customers.
Anomaly Detection in Machine Learning:
An approach to data analysis and Machine Learning called “anomaly detection,” also referred to as “outlier detection,” focuses on finding data points or patterns that considerably differ from what is considered to be “normal” or anticipated behaviour.
Observations that deviate from the majority of the data are known as anomalies and might take the shape of occurrences, trends, or events that differ from customary or expected behaviour.
Finding anomalous occurrences that might point to intriguing or potentially significant events is the aim of anomaly detection. Anomalies could be a sign of many different things, including fraud, mistakes, flaws, health problems, security breaches, and more. In many fields, finding anomalies can yield insightful data and useful information.
Anomaly detection Machine Learning example:
Given below are the Machine Learning anomaly detection examples that you need to know about:
Network Intrusion Detection:
Anomaly detection Machine Learning algorithms is used to monitor network traffic and identify unusual patterns that might indicate a cyberattack or unauthorised access. For instance, sudden spikes in data traffic or unusual communication patterns between devices can be flagged as anomalies.
Anomaly detection can be applied to patient monitoring data to identify irregularities in vital signs. This could include detecting unusual heart rhythms in ECG data or unexpected variations in blood pressure that might indicate a health issue.
Manufacturing Quality Control:
Anomaly detection can be used to monitor the output of manufacturing processes. It can identify faulty products by analysing sensor data, such as detecting defects in the shape or size of products on an assembly line.
Energy Usage Monitoring:
Anomaly detection is used to identify abnormal energy consumption patterns in industrial or residential settings. Sudden spikes or drops in energy usage can indicate equipment malfunction or energy theft.
Fraud Detection in Financial Transactions:
Anomaly detection techniques are used to identify fraudulent credit card transactions. Transactions that deviate from a user’s usual spending patterns or involve unusual locations can be flagged for further investigation.
Aircraft Engine Performance Monitoring:
Anomaly detection is used to monitor aircraft engine health. By analysing data from various sensors on the engine, deviations from normal operating conditions can be detected, allowing maintenance crews to address potential issues before they lead to failures.
E-commerce Customer Behaviour:
Anomaly detection can be used to identify unusual patterns in customer behaviour on e-commerce platforms. For instance, sudden changes in purchase habits or unusually high cart abandonment rates might indicate fraud or other issues.
Anomaly detection is used to monitor environmental factors like air quality and water pollution. Unusual variations in pollutant levels or other environmental parameters can be indicative of an incident or pollution source.
Supply Chain Anomalies:
Anomaly detection is applied to supply chain data to identify disruptions or irregularities. Unexpected delays in shipping, drastic changes in order quantities, or sudden supplier changes can be flagged as anomalies.
Server Log Analysis:
Anomaly detection is used to monitor server logs and identify unusual patterns that might indicate a security breach or system failure. This could include sudden spikes in failed login attempts or unusual patterns of resource usage.
These examples highlight the versatility of anomaly detection in various domains. The specific techniques and algorithms used can vary based on the nature of the data and the problem at hand.
Anomaly Detection Machine Learning Techniques:
Unsupervised Anomaly Detection:
- Artificial Neural Networks (ANNs): Autoencoders, a type of neural network, can be used for unsupervised anomaly detection. An autoencoder consists of an encoder network that maps input data to a lower-dimensional representation, and a decoder network that reconstructs the input from the lower-dimensional representation.
During training, the model learns to minimise the reconstruction error. Anomalies, being different from normal data, result in higher reconstruction errors.
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN): DBSCAN is a density-based clustering algorithm. It identifies regions of high data point density as clusters and flags points with low densities as anomalies. Points that don’t belong to any cluster or are in low-density regions are considered anomalies.
- Isolation Forest: The Isolation Forest algorithm creates a random partition of the data by selecting features and random split values. Anomalies can be isolated more quickly as they require fewer splits to be separated from the majority of the data. This algorithm is efficient and effective for high-dimensional datasets.
- Gaussian Mixture Models (GMM): GMM represents the data distribution as a mixture of several Gaussian distributions. Anomalies might have low probabilities under the fitted GMM, as they deviate from the common Gaussian patterns observed in normal data.
Supervised Anomaly Detection:
- Support Vector Machines (SVM): In a supervised context, SVM is trained to find a hyperplane that best separates normal instances from anomalies. Anomalies are treated as the minority class, and the model aims to maximise the margin between the two classes.
- Random Forests: Random Forests can be adapted for anomaly detection by treating it as a classification problem. An ensemble of decision trees is trained on both normal and anomalous data. Instances that are difficult to classify (misclassified or those in the minority class) receive higher outlier scores, indicating they might be anomalies.
- k-Nearest Neighbors (k-NN): In the supervised approach, k-NN assigns labels to instances based on their k-nearest neighbours. Anomalies are assigned to the class where they have the fewest neighbours, or instances that are far from their neighbours can be identified as anomalies.
Semi-Supervised Anomaly Detection:
- Pre-trained Models: Models that are pre-trained on a large dataset, like deep learning models trained on ImageNet, can be fine-tuned for anomaly detection on a specific problem. Anomalies might lead to deviations from the normal patterns the model has learned.
- Transfer Learning: Transfer learning involves using a pre-trained model from one domain to solve a related task in another domain. By fine-tuning the model with your data, you can leverage its learned features for anomaly detection.
Semi-supervised techniques leverage a combination of labelled normal data and unlabeled data to enhance anomaly detection performance. Choosing the right technique depends on the characteristics of your data, the distribution of anomalies, and the available resources for training and evaluation.
It’s important to experiment and iterate to find the most effective approach for your specific use case.
How to do Anomaly Detection using Machine Learning in Python?
Here’s a detailed step-by-step guide on how to perform anomaly detection using Machine Learning in Python. We’ll use a simple example of credit card fraud detection and the Isolation Forest algorithm for this demonstration.
Step 1: Import Libraries Start by importing the necessary libraries.
Step 2: Load and Explore Data Load your dataset and explore its structure and content.
Step 3: Data Preprocessing Preprocess the data by handling missing values and scaling numerical features.
Step 4: Model Training Train the Isolation Forest model on the training data.
Step 5: Anomaly Detection and Evaluation: Detect anomalies in the testing data and evaluate the model’s performance.
Step 6: Interpretation and Tuning Interpret the results, analyse the classification report, and adjust the parameters if needed.
- The contamination parameter in the Isolation Forest determines the expected proportion of anomalies in the data. You can adjust this based on your dataset’s characteristics.
- You can explore other algorithms like one-class SVM, autoencoders, or different ensemble methods for anomaly detection.
Step 7: Deployment Once you’re satisfied with the model’s performance, you can deploy it to detect anomalies in real-time data. This might involve setting up a pipeline to preprocess incoming data and use the trained model to predict anomalies.
Remember that anomaly detection is a continuous process. As new patterns of anomalies emerge, you’ll need to update and retrain your model to ensure its effectiveness.
In conclusion, we have provided you with an in-depth understanding of Anomaly Detection Machine Learning. Make sure that you learn the entire process thoroughly with much practice using Python.
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