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Anomaly detection is the process of identifying exceptional events, items, or observations that deviate from typical behaviors or patterns. This process plays a pivotal role in various domains. These anomalies are often referred to as standard deviations, outliers, noise, novelties, or exceptions.
In network anomaly detection and network intrusion detection the term “interesting events” is not necessarily rare but indicate unusual occurrences. For instance, sudden surges in activity, while not rare, are still considered noteworthy. Traditional statistical anomaly detection methods might not flag these abrupt spikes in activity as outliers. In such cases, cluster analysis algorithms can be more effective in detecting these microclusters of data.
Anomaly detection plays a crucial role in various domains, including cybersecurity, finance, and healthcare. It helps companies prevent fraudulent activities, detect network intrusions, and identify financial anomalies. With the ability to proactively detect deviations from the norm, anomaly detection empowers businesses to mitigate risks. By doing all of that it ensures data integrity and helps individuals make informed decisions.
Anomaly detection techniques can be categorized into three primary types: unsupervised, semi-supervised, and supervised. The choice of the appropriate method depends on the availability of labels in the dataset. Let’s break them down:
Supervised Anomaly Detection
This approach requires a dataset with a complete set of “normal” and “abnormal” labels for a classification algorithm to operate effectively. Training is a key aspect of anomaly detection, akin to conventional pattern recognition. However, this method must deal with a significant class imbalance. As a result, not all statistical classification algorithms are well-suited to address this inherent imbalance in the process.
Semi-Supervised Anomaly Detection
Semi-supervised methods leverage a labeled training dataset representing normal behavior to create a model. This model is then employed to detect anomalies by assessing how likely the model is to generate any encountered instance.
Unsupervised Anomaly Detection
Unsupervised methods identify anomalies in an unlabeled test dataset solely based on the intrinsic properties of the data. The underlying assumption is that, in most cases, the majority of instances in the dataset are normal. Anomaly detection algorithms identify instances that show the least congruence with the rest of the dataset.
The wide array of techniques caters to the diverse needs and challenges of anomaly detection. These techniques encompass generative and discriminative approaches and include clustering-based, density-based, and support vector machine-based methods. Selecting the most appropriate technique depends on the specific use case and characteristics of the dataset. Anomalies can be expressed in diverse forms, requiring customized approaches for detection and mitigation.
Anomaly detection plays a crucial role in observability by helping companies monitor and maintain the health and performance of their systems, applications, and infrastructure. Here are some key use cases in the context of observability: