Algorithm selection for Anomaly Detection

Anomalies can be defined as observations which deviate sufficiently from most observations in the data set to consider that they were generated by a different, not normal, generative process. Anomaly is any observation which deviates so much from other observations in the data set so as to arouse suspicion. In brief, anomalies are _rare and significantly different _observations within a data set.

Anomaly detection algorithms are now used in many application domains for Intrusion detection, Fraud Detection, Data leakage prevention, Data Quality, Surveillance & Monitoring. As we see these are wide variety of applications, some require very fast, near real time anomaly detection whereas some require very high performance due to high cost of missing an anomaly. Anomaly detection techniques are most commonly used to detect fraud, where malicious attempts/transactions often differ from most nominal cases. Outlined below are the different types of anomalies:

Point anomaly: Single anomalous instances in a larger dataset

**Collective anomaly: **If an anomalous situation is represented as a set of many instances, this is called a collective anomaly.

**Contextual anomaly: **In contextual anomalies, point can be seen as normal but when a given context is taken into account, the point turns out to be an anomaly.

Anomaly Detection Algorithms

The solution to anomaly detection can be framed in all three types of machine learning methods — Supervised, Semi-supervised and Unsupervised, depending on the type of data available. Supervised learning algorithms can be used for anomaly detection when anomalies are already known and labelled data is available. These methods are particularly expensive when the labeling has to be done manually. Unbalanced classification algorithms such as Support Vector Machines (SVM) or Artificial Neural Networks (ANN) can be used for supervised anomaly detection.

Semi-supervised anomaly Detection uses labelled data consisting only of normal data without any anomalies. The basic idea is, that a model of the normal class is learned and any deviations from that model can be said to be anomalies. Popular algorithms: Auto-Encoders, Gaussian Mixture Models, Kernel Density Estimation.

Unsupervised learning methods are most commonly used to detect anomalies, the following chart outlines major families of algorithms and algorithms which can be used for anomaly detection.

**K-Nearest Neighbor (kNN): **kNN is a neighbor based method which was primarily designed to identify outliers. For each data point, the whole set of data points is examined to extract the k items that have the most similar feature values: these are the k nearest neighbors (NN). Then, the data point is classified as anomalous if the majority of NN was previously classified as anomalous.

K-NN

**Local Outlier Factor(LOF): **Local Outlier Factor is a density-based method designed to find local anomalies. For each data point, the NN are computed. Then, using the computed neighborhood, the local density is computed as the Local Reachability Density (LRD). Finally, the LOF score is computed by comparing the LRD of a data point with the LRD of the previously computed NN.

Local Outlier Factor

**Connectivity Based Outlier factor (COF): **Connectivity-based Outlier Factor (COF) differs from LOF in the computation of the density of the data points, since it also considers links between data points. To such extent, this method adopts a shortest-path approach that calculates a chaining distance, using a minimum spanning tree

Connectivity Based Outlier factor

**K-Means: **K-means Clustering is a popular clustering algorithm that groups data points into k clusters by their feature values. Scores of each data point inside a cluster are calculated as the distance to its centroid. Data points which are far from the centroid of their clusters are labeled as anomalies.

K-means Clustering

**Robust Principal Component Analysis(rPCA): **Principal component analysis is a commonly used technique for detecting sub-spaces in datasets. It also serves as an anomaly detection technique, such that deviations from the normal sub-spaces may indicate anomalous instances. Once the principal components are determined major components show global deviations from the majority of the data whereas using minor components can indicate smaller local deviations.

Robust Principal Component Analysis

**One Class SVM: **One-class Support Vector Machine algorithm aims at learning a decision boundary to group the data points. It can be used for unsupervised anomaly detection, the one-class SVM is trained with the dataset and then each data point is classified considering the normalized distance of the data point from the determined decision boundary

One Class SVM

**Isolation Forest: **Isolation Forest structures data points as nodes of an isolation tree, assuming that anomalies are rare events with feature values that differ a lot from expected data points. Therefore, anomalies are more susceptible to isolation than the expected data points, since they are isolated closer to the root of the tree instead of the leaves. It follows that a data point can be isolated and then classified according to its distance from the root of the tree.

Isolation Forest

**Angle Based Outlier detection (ABOD): **Angle Based Outlier detection (ABOD) relates data to high-dimensional spaces, using the variance in the angles between a data point to the other points as anomaly score. The angle-based outlier detection (ABOD) method provides an good alternative in identifying outliers in high-dimensional spaces

Angle Based Outlier detection

#artificial-intelligence #data-science #anomaly-detection #machine-learning #fraud-detection

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These Tips Will Help You Step Up Anomaly Detection Using ML

In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two.

A common need when you analyzing real-world data-sets is determining which data point stand out as being different from all other data points. Such data points are known as anomalies, and the goal of anomaly detection (also known as outlier detection) is to determine all such data points in a data-driven fashion. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously unknown, underlying process.

#machine-learning #machine-learning-algorithms #anomaly-detection #detecting-data-anomalies #data-anomalies #machine-learning-use-cases #artificial-intelligence #fraud-detection

Credit Card Fraud Detection via Machine Learning: A Case Study

This is the second and last part of my series which focuses on Anomaly Detection using Machine Learning. If you haven’t already, I recommend you read my first article here which will introduce you to Anomaly Detection and its applications in the business world.

In this article, I will take you through a case study focus on Credit Card Fraud Detection. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. So the main task is to identify fraudulent credit card transactions by using Machine learning. We are going to use a Python library called PyOD which is specifically developed for anomaly detection purposes.

#machine-learning #anomaly-detection #data-anomalies #detecting-data-anomalies #fraud-detection #fraud-detector #data-science #machine-learning-tutorials

Algorithm selection for Anomaly Detection

Anomalies can be defined as observations which deviate sufficiently from most observations in the data set to consider that they were generated by a different, not normal, generative process. Anomaly is any observation which deviates so much from other observations in the data set so as to arouse suspicion. In brief, anomalies are _rare and significantly different _observations within a data set.

Anomaly detection algorithms are now used in many application domains for Intrusion detection, Fraud Detection, Data leakage prevention, Data Quality, Surveillance & Monitoring. As we see these are wide variety of applications, some require very fast, near real time anomaly detection whereas some require very high performance due to high cost of missing an anomaly. Anomaly detection techniques are most commonly used to detect fraud, where malicious attempts/transactions often differ from most nominal cases. Outlined below are the different types of anomalies:

Point anomaly: Single anomalous instances in a larger dataset

**Collective anomaly: **If an anomalous situation is represented as a set of many instances, this is called a collective anomaly.

**Contextual anomaly: **In contextual anomalies, point can be seen as normal but when a given context is taken into account, the point turns out to be an anomaly.

Anomaly Detection Algorithms

The solution to anomaly detection can be framed in all three types of machine learning methods — Supervised, Semi-supervised and Unsupervised, depending on the type of data available. Supervised learning algorithms can be used for anomaly detection when anomalies are already known and labelled data is available. These methods are particularly expensive when the labeling has to be done manually. Unbalanced classification algorithms such as Support Vector Machines (SVM) or Artificial Neural Networks (ANN) can be used for supervised anomaly detection.

Semi-supervised anomaly Detection uses labelled data consisting only of normal data without any anomalies. The basic idea is, that a model of the normal class is learned and any deviations from that model can be said to be anomalies. Popular algorithms: Auto-Encoders, Gaussian Mixture Models, Kernel Density Estimation.

Unsupervised learning methods are most commonly used to detect anomalies, the following chart outlines major families of algorithms and algorithms which can be used for anomaly detection.

**K-Nearest Neighbor (kNN): **kNN is a neighbor based method which was primarily designed to identify outliers. For each data point, the whole set of data points is examined to extract the k items that have the most similar feature values: these are the k nearest neighbors (NN). Then, the data point is classified as anomalous if the majority of NN was previously classified as anomalous.

K-NN

**Local Outlier Factor(LOF): **Local Outlier Factor is a density-based method designed to find local anomalies. For each data point, the NN are computed. Then, using the computed neighborhood, the local density is computed as the Local Reachability Density (LRD). Finally, the LOF score is computed by comparing the LRD of a data point with the LRD of the previously computed NN.

Local Outlier Factor

**Connectivity Based Outlier factor (COF): **Connectivity-based Outlier Factor (COF) differs from LOF in the computation of the density of the data points, since it also considers links between data points. To such extent, this method adopts a shortest-path approach that calculates a chaining distance, using a minimum spanning tree

Connectivity Based Outlier factor

**K-Means: **K-means Clustering is a popular clustering algorithm that groups data points into k clusters by their feature values. Scores of each data point inside a cluster are calculated as the distance to its centroid. Data points which are far from the centroid of their clusters are labeled as anomalies.

K-means Clustering

**Robust Principal Component Analysis(rPCA): **Principal component analysis is a commonly used technique for detecting sub-spaces in datasets. It also serves as an anomaly detection technique, such that deviations from the normal sub-spaces may indicate anomalous instances. Once the principal components are determined major components show global deviations from the majority of the data whereas using minor components can indicate smaller local deviations.

Robust Principal Component Analysis

**One Class SVM: **One-class Support Vector Machine algorithm aims at learning a decision boundary to group the data points. It can be used for unsupervised anomaly detection, the one-class SVM is trained with the dataset and then each data point is classified considering the normalized distance of the data point from the determined decision boundary

One Class SVM

**Isolation Forest: **Isolation Forest structures data points as nodes of an isolation tree, assuming that anomalies are rare events with feature values that differ a lot from expected data points. Therefore, anomalies are more susceptible to isolation than the expected data points, since they are isolated closer to the root of the tree instead of the leaves. It follows that a data point can be isolated and then classified according to its distance from the root of the tree.

Isolation Forest

**Angle Based Outlier detection (ABOD): **Angle Based Outlier detection (ABOD) relates data to high-dimensional spaces, using the variance in the angles between a data point to the other points as anomaly score. The angle-based outlier detection (ABOD) method provides an good alternative in identifying outliers in high-dimensional spaces

Angle Based Outlier detection

#artificial-intelligence #data-science #anomaly-detection #machine-learning #fraud-detection

Boxplot for anomaly detection

In the previous article, I wrote about outlier detection using a simple statistical technique called Z-score. While that’s an easy way to create a filter for screening outliers, there’s even a better way to do it — using boxplots.

Boxplots are an excellent statistical technique to understand the distribution, dispersion and variation of univariate and categorical data— all in a single plot.

The purpose of this article is to introduce boxplot as a tool for outlier detection, and I’m doing so focusing on the following areas:

• the statistical intuition behind boxplots
• how they are used in outlier detection
• a tiny bit of programming

#machine-learning #data-science #anomaly-detection #outlier-detection #algorithms

Introduction to Anomaly Detection Using PyCarat

What is an Anomaly?

An anomaly by definition is something that deviates from what is standard, normal, or expected.

When dealing with datasets on a binary classification problem, we usually deal with a balanced dataset. This ensures that the model picks up the right features to learn. Now, what happens if you have very little data belonging to one class, and almost all data points belong to another class?

In such a case, we consider one classification to be the ‘normal’, and the sparse data points as a deviation from the ‘normal’ classification points.

For example, you lock your house every day twice, at 11 AM before going to the office and 10 PM before sleeping. In case a lock is opened at 2 AM, this would be considered abnormal behavior. Anomaly detection means predicting these instances and is used for Intrusion Detection, Fraud Detection, health monitoring, etc.

In this article, I show you how to use pycaret on a dataset for anomaly detection.

What is PyCaret?

PyCaret is an open-source, low-code machine learning library in Python that aims to reduce the cycle time from hypothesis to insights. It is well suited for seasoned data scientists who want to increase the productivity of their ML experiments by using PyCaret in their workflows or for citizen data scientists and those **new to data science **with little or no background in coding. PyCaret allows you to go from preparing your data to deploying your model within seconds using your choice of notebook environment.

So, simply put, pycaret makes it super easy for you to visualize and train a model on your datasets within 3 lines of code!

So let’s dive in!

#anomaly-detection #machine-learning #anomaly #fraud-detection #pycaret