Anomaly Detection – Definition and Overview

Introduction

Anomaly detection scans specific data points and detects unusual events appearing suspicious as they deviate from a defined pattern or behavior. Moreover, it is nothing new, as manual tracking has become impractical because of the increase in data.

Why Anomaly Detection is Important?

Network administrators must identify and respond to changing operating conditions. Any nuance in the operating conditions of data centers or cloud applications may indicate obnoxious levels of business risk. On the contrary, certain deviations may suggest positive growth.

Data is the company’s essence, and compromising it could put any business at risk. Without detecting anomalies, organizations may lose revenue and brand value that took years to build.

In the case of a business facing security breaches and loss of confidential customer information, it risks losing customer trust, which may be irreparable. Therefore, this process is important for discovering essential business information and maintaining basic operations.

Benefits of Anomaly Detection:

1. The process has the advantage of identifying and addressing problems before disseminating them to other parts of the system.
2. This practical approach saves cost, as the interference is focused on a specific area rather than the entire system.
3. Customer service is vital to detect anomalies, specifically when a system compromise could have noteworthy consequences.
4. Internal and external customers may be unpleasantly affected in a system breach. Hence, anomaly-detecting methods are helpful.
5. Detecting anomalies becomes a crucial strategy to mitigate this threat and, more importantly, to preserve trust across all customer segments.

Challenges of Anomaly Detection:

Anomaly detection encounters numerous challenges across different applications and industries. Some of the common challenges include:

• Anomaly detection in data science is valuable when identifying true outliers, requiring system training before practical use to avoid abundant alerts.
• An anomaly finder takes a good amount of time to form a reliable baseline for data across a company’s entire IT infrastructure, notably if labeled data sets are lacking.
• Data quality issues and small training samples can reduce the effectiveness of anomaly detection algorithms, leading to unreliable models that may miss significant outliers.
• Without a high-quality dataset, anomaly detection systems may develop unreliable capabilities, potentially resulting in the oversight of glaring anomalies.
• Anomaly detection systems can be too sensitive if not provided with enough data to determine the degree of deviation from the norm that defines a true outlier.

Methods of Anomaly Detection:

Establishments can train their machine learning algorithms using various anomaly detection and prevention methods. Some of the most common anomaly detection techniques are:

Clustering-Based Method:

Clustering-based method, a popular unsupervised learning approach, depends on the assumption that related data points form clusters around local centroids. In addition, the widely-used K-means algorithm creates ‘k’ clusters, allowing users to identify anomalies as cases falling outside these groups.

As unsupervised, clustering doesn’t require labeled data. It can be applied to capture anomalous data classes by utilizing preceding clusters to establish a threshold.

However, clustering faces challenges with time series data, where generating fixed clusters may not effectively capture temporal evolution. Despite its limitations, clustering remains valuable for various AD applications in datasets with distinct clusters.

Density-Based Method:

Density-based anomaly detection relies on labeled data, supposing regular points cluster densely, while anomalies are limited and distant. K-nearest neighbor (K-NN) & Local Outlier Factor (LOF) are two algorithms for this assessment.

Moreover, K-NN is a non-parametric, supervised technique that uses distance metrics like Euclidean or Hamming to regress or classify data.

LOF, measuring relative data density through reachability distance, is another method. Consequently, both contribute to detecting anomalies, but challenges include obtaining labeled data, addressing imbalanced datasets, and adapting to dynamic environments.

Time-Series Methods:

Change Point Detection identifies potential anomalies by signaling unexpected changes in time-series data. On the contrary, Autoencoders, which are neural network-based models, operate in time-series data by reconstructing standard patterns.

Furthermore, these models highlight deviations from the established patterns, effectively pinpointing anomalies within the temporal context.

Conclusion:

To summarize, anomaly detection observes machine learning methods to detect outliers or unexpected patterns in data. Supervised and unsupervised learning methods are implemented to comprehend these unexpected situations.

Moreover, Isolation forest, single-layer SVM, Autoencoders, and Gaussian mixture models are prevalent methods for anomaly detection. Anomalies can be detected using statistical, machine learning, or rule-based methods.