Anomaly Detection Techniques
Simple Statistical Methods
The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of a distribution, including mean, median, mode, and quantiles. Let's say the definition of an anomalous data point is one that deviates by a certain standard deviation from the mean. Traversing mean over time-series data isn't exactly trivial, as it's not static. You would need a rolling window to compute the average across the data points. Technically, this is called a rolling average or a moving average, and it's intended to smooth short-term fluctuations and highlight long-term ones. Mathematically, an n-period simple moving average can also be defined as a "low pass filter." (A Kalman filter is a more sophisticated version of this metric; you can find a very intuitive explanation of it here.)
Challenges
The low pass filter allows you to identify anomalies in simple use cases, but there are certain situations where this technique won't work. Here are a few:
The data contains noise which might be similar to abnormal behavior, because the boundary between normal and abnormal behavior is often not precise.
The definition of abnormal or normal may frequently change, as malicious adversaries constantly adapt themselves. Therefore, the threshold based on moving average may not always apply.
The pattern is based on seasonality. This involves more sophisticated methods, such as decomposing the data into multiple trends in order to identify the change in seasonality.
Machine Learning-Based Approaches
Below is a brief overview of popular machine learning-based techniques for anomaly detection.
Density-Based Anomaly Detection
Density-based anomaly detection is based on the k-nearest neighbors algorithm.
Assumption: Normal data points occur around a dense neighborhood and abnormalities are far away.
The nearest set of data points are evaluated using a score, which could be Eucledian distance or a similar measure dependent on the type of the data (categorical or numerical). They could be broadly classified into two algorithms:
K-nearest neighbor: k-NN is a simple, non-parametric lazy learning technique used to classify data based on similarities in distance metrics such as Eucledian, Manhattan, Minkowski, or Hamming distance.
Relative density of data: This is better known as local outlier factor (LOF). This concept is based on a distance metric called reachability distance.
Clustering-Based Anomaly Detection
Clustering is one of the most popular concepts in the domain of unsupervised learning.
Assumption: Data points that are similar tend to belong to similar groups or clusters, as determined by their distance from local centroids.
K-means is a widely used clustering algorithm. It creates 'k' similar clusters of data points. Data instances that fall outside of these groups could potentially be marked as anomalies.
Support Vector Machine-Based Anomaly Detection
A support vector machine is another effective technique for detecting anomalies. A SVM is typically associated with supervised learning, but there are extensions (OneClassCVM, for instance) that can be used to identify anomalies as an unsupervised problems (in which training data are not labeled). The algorithm learns a soft boundary in order to cluster the normal data instances using the training set, and then, using the testing instance, it tunes itself to identify the abnormalities that fall outside the learned region.
Depending on the use case, the output of an anomaly detector could be numeric scalar values for filtering on domain-specific thresholds or textual labels (such as binary/multi labels).