What is the difference between supervised and unsupervised anomaly detection?

Supervised anomaly detection is generally performed with neural networks or ensembles like Random Forest, XGBoost & trained on labeled data. Anomaly detection has to inherently deal with imbalance dataset, anomalies constituting the minority class - say normal class 0; anomaly class 1. Accordingly supervised anomaly detection methods form two categories 1. Those that try to fix the imbalance - either by data resampling or adjusting model weights. 2. Those that do not fix the imbalance. Here, the model is trained only on normal labelled data; anomalies are completely ignored. As long as the training data captures all types of normal data class, we get good performance in detection without data resampling or identifying all anomaly classes

In addition to the mentioned points, supervised anomaly detection typically requires significant human effort and domain expertise to accurately label instances of anomalies in the training data. Moreover, it may struggle to generalize well to new or unseen types of anomalies not present in the training set, limiting its applicability in detecting emerging threats or evolving patterns of abnormal behavior. Despite these limitations, supervised anomaly detection remains a valuable approach for detecting known anomalies with high precision and recall when sufficient labeled data is available.

Supervised anomaly detection involves training a model on a labeled dataset, where the labels indicate whether an instance is normal or an anomaly. Logistic regression, SVMs, or neural networks can be employed, using the labeled examples to learn the characteristics that distinguish anomalies from normal instances. This approach is effective in fraud detection in banking, where historical transactions are labeled as fraudulent or legitimate, enabling the model to identify similar patterns in future transactions accurately.

Supervised anomaly detection relies on labeled data,where anomalies are explicitly identified and used to train a model.The model learns to distinguish between normal and anomalous instances based on the provided labels.In contrast, unsupervised anomaly detection operates without labeled data.It identifies anomalies solely based on the inherent structure and characteristics of the data.Unsupervised methods aim to identify patterns or outliers that deviate significantly from the majority of the data points,without relying on predefined anomaly labels.While supervised methods may achieve higher accuracy with labeled data,unsupervised methods are more versatile and can detect previously unseen anomalies without the need for labeled examples

Supervised anomaly detection relies on a labelled dataset containing both normal and anomalous instances. The model learns to identify the defining characteristics of known anomalies. This approach requires a substantial and well-labelled dataset and excels at detecting the specific anomalies it was trained on. However, it might struggle with new, unseen anomaly types and obtaining a comprehensive labelled dataset can be challenging.

Unsupervised anomaly detection is most practical in industry when labeled data is pipedream. Following types of methods exist: 1. Statistical: z-score, Modified z-score, Mean Abs Deviation, Mahalanobis distance, Minimum Covariance Determinant 2. Clustering: spectral clustering, spatial/connectivity clustering like K-Means GMM Hierarchial, spatial density clustering like DBSCAN HDBSCAN OPTICS 3. Reconstruction methods where original data is rebuilt after passing through a latent representation bottleneck as in Principal Component Analysis & Variational Autoencoder 4. Spatial proximity methods using widely different underlying algorithm like Isolation Forest, Local Outlier Factor, Connectivity based Outlier Factor, Local Correlation Integral

Unsupervised anomaly detection involves identification of abnormal data behavior without the use of labeled examples. The algorithm is trained solely on a dataset containing normal instances, without any prior knowledge of anomalies. The algorithm learns the typical patterns and structures inherent in the normal data during the training phase. Then, during testing or deployment, it identifies instances that deviate significantly from these learned patterns as anomalies. Unsupervised anomaly detection is particularly useful when labeled data is scarce or unavailable. However, it may be more challenging to differentiate between true anomalies and benign variations in the data.

Unsupervised anomaly detection works without labeled examples. It trains on a dataset assumed to be primarily normal, learning the typical patterns of behavior. Anything deviating significantly from this "normal" pattern is flagged as an anomaly. This method allows the detection of new types of anomalies but can sometimes produce false positives, especially if the definition of "normal" is fuzzy or in complex datasets.

Unsupervised anomaly detection works without labeled data, relying on the intrinsic properties of the data to detect anomalies. K-means clustering, isolation forests, or autoencoders are used to model the normal behavior of data points, identifying outliers that do not fit the learned model. This is useful in scenarios where anomalies are not well-defined or in exploratory settings, such as detecting novel network intrusions or identifying rare events in environmental sensor data.

From the name we can understand it uses Unsupervised methods. Instead of using labeled data like Supervised models does, It uses Statistical relationship or Distance methods to finds the anomalies form the entire data. For Ex: It uses methods like Clustering or Nearest Neighbour to find the anomalies. It uses methods like Z-score which uses statistical information such as mean and standard deviation to find the anomalies. The benefits of Unsupervised method is that it can handle unlabelled data.

While merging the outputs of the supervised and unsupervised learning phases to create a hybrid model, we can adjust the weighting of the labelled data to prioritize its influence. This step can be powerful as it grants flexibility and enables us to effectively leverage prior knowledge about known anomalies or normal behavior.

From the name we can understand that this approach uses a combination of labeled and unlabelled data to train the model. The model learns to identify anomalies in the unlabelled data based on what it has learned from the labeled data. The main advantage of semi-supervised anomaly detection is that it can reduce the labeling effort and cost, and improve the generalization and robustness of the model.

Semi-supervised anomaly detection is most workable option in industry. Two methods 1. Labeling method: Use incompletely labeled dataset to label connected neighbors (by treating them as connected by weighted edges of a graph) & repeat process till most data is labeled. 2. Discovery method: Execute multiple unsupervised anomaly detection algorithms with different hyperparameters first to filter out vast majority of normal data. The auto-discovered anomalies from each algorithm/hyperparameter combo are presented to a subject matter expert to validate. A Jaccard similarity between the two anomaly sets - those auto identified by algorithm & those marked by human helps to arrive at the appropriate feature representation space for anomalies

Semi-supervised anomaly detection uses a small set of labeled data, typically normal instances, to guide the learning process, along with a larger set of unlabeled data. The model learns to recognize the patterns of the normal instances and flags deviations from these patterns as anomalies. This is effective when anomalies are rare or not well-represented in the data, like for detecting new types of malware based on known benign software behavior.

Semi-supervised anomaly detection sits between the two. It uses a small set of labelled normal data to establish a baseline for normal behaviour and then applies unsupervised techniques to find deviations. This balances the advantages of supervised and unsupervised learning. It reduces the need for extensive labelled data while potentially being better at discovering unseen anomalies compared to unsupervised methods alone.

Choose supervised methods if you can gather a well-labelled dataset, focus on known anomaly types, and want high accuracy for those types. Unsupervised methods are best when labels are scarce, you anticipate novel anomalies, or data patterns shift over time. Semi-supervised learning acts as a middle ground when we can partially label normal behaviour.

In real life, fully labelled dataset is a luxury that only the chosen few enjoy. Hence a combination of unsupervised & semi-supervised algorithms are used with a human in the loop. Human aspect allows to incorporate 2 things: 1. To include a context for the anomaly rather than plainly relying on technical/mathematical/statistics characteristics of anomaly identification. This happens in a lot of cases when a data is technically an anomaly, but in business, is plainly a rarely occuring harmless datapoint. 2. Active Learning. Labeling happens on demand here & the model incrementally evolves over time into robust representation.

Supervised anomaly detection is more suitable when labeled data is abundant and the goal is to precisely identify anomalies with high confidence. Unsupervised anomaly detection is preferred when labeled data is scarce or when the anomalies of interest may evolve over time, making it difficult to maintain labeled datasets. Semi-supervised anomaly detection strikes a balance between the two approaches, leveraging the advantages of both labeled and unlabeled data.

Supervised anomaly detection is preferred when high-quality labeled data is available and the types of anomalies are known and consistent over time, such as in credit card fraud detection. Unsupervised detection is suited for scenarios where labeling is impractical or when seeking to identify unknown types of anomalies, like in unsupervised network traffic analysis for cybersecurity. Semi-supervised methods bridge the gap, which is applicable in situations like manufacturing defect detection, where regular operation is well-understood. However, anomalies may not be fully represented in the data.

Supervised anomaly detection involves training a model on labeled data to distinguish between normal and abnormal instances. For example, in email filtering, the system is trained with examples of spam and non-spam emails, enabling it to identify and flag new spam emails effectively. Unsupervised anomaly detection, on the other hand, does not use labeled data. It identifies anomalies by looking for data points that significantly deviate from the majority. An example is monitoring website traffic; an unsupervised system might detect a sudden spike in traffic as an anomaly, potentially indicating a cyber attack, without prior knowledge of what typical attack patterns look like.

To ensure interpretability of anomaly detection, one should use algorithms that do not extract features or perform dimensionality reduction inspite of their higher accuracy during inferencing. Few best practices with this are: 1. Use complex methods to identify anomalies, & then use the discovered anomalies to train simpler interpretable algorithm like Random Forest 2. Spatial, connectivity & density based clustering without dim-reduction are also best friends in anomaly detection. But remember that they are offline algorithms & cannot be used to inference streaming/online data due to the computation cost. The anomaly knowledge extracted with clustering should be incorporated into supervised algorithms that are "training friendly"

Incorporating domain knowledge helps choose the right method and define relevant anomalies. Evaluate your model carefully, especially for unsupervised methods where you typically lack labelled anomalies. Experiment with different algorithms to see which suits your data.

For effective anomaly detection, it's essential to preprocess and normalize data to ensure consistency. Regularly updating the model with new data helps maintain its accuracy over time. Balancing the dataset to handle class imbalance between normal and anomalous instances can improve performance in supervised learning. In unsupervised learning, selecting the right features and tuning algorithm parameters based on domain knowledge are key. Ensuring the labeled data accurately represents normal conditions is essential for semi-supervised approaches.

Supervised anomaly detection methods involves using features directly and are often more interpretable. Unsupervised methods involves extracting different combination of features and latent space representation. This makes them accurate but also highly interpretable. Interpretability is an aspect often overlooked in anomaly detection. This becomes crucial when prescriptive measures needs to be recommended for prevention of anomalies. Examples include looking at the chemical composition of coal to aovid, tweaking of boiler sensors in a coal based powerplant when edge conditions have to be avoided. Another use case anomaly visualization after dimensionality reduction with tSNE, UMAP etc. helps to "see" but fails to decipher "why"

Supervised anomaly detection relies on labeled data to train a model to distinguish between normal and abnormal instances. It requires predefined anomalies for comparison, offering high precision but limited scalability. In contrast, unsupervised anomaly detection doesn't rely on labeled data. It detects anomalies based on deviations from normal behavior, making it more scalable but potentially less precise. It's like having a radar that flags anything unusual without needing prior examples. Both methods have their strengths, but the choice depends on the specific use case and available data.