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Anomalous Activity Detection

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May 14, 2024 3 minute read

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Anomalous Activity Detection (AAD) is a powerful technique used to identify unusual patterns or events that deviate significantly from the norm. This capability is crucial in various domains, including cybersecurity, fraud detection, and healthcare diagnostics, where timely detection of anomalies can prevent or mitigate potential threats or issues.

Why Learn Anomalous Activity Detection?

There are several compelling reasons to learn about AAD:

Path to Anomalous Activity Detection

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We've curated two courses to help you on your path to Anomalous Activity Detection. Use these to develop your skills, build background knowledge, and put what you learn to practice.

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Cyber Threat Hunting

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Threat Hunting: Hypothesize and Plan

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Reading list

We've selected three books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Anomalous Activity Detection.

Outlier Analysis

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The book provides a comprehensive overview of anomaly detection techniques. It covers both traditional and state-of-the-art methods, including statistical, machine learning, and deep learning approaches. The author renowned expert in the field of anomaly detection.

Outlier Detection for Temporal Data

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Focuses on detecting anomalies in high-dimensional data. It covers a variety of techniques for detecting anomalies in high-dimensional data, including statistical, machine learning, and deep learning methods.

Outlier Detection for Temporal Data

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Outlier Detection for Temporal Data

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Data Mining

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Surveys the state-of-the-art in data stream clustering. It covers a variety of clustering algorithms for data streams, including online, offline, and semi-online algorithms.

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