Applying unsupervised machine learning to find ‘randomly generated domains.
Authors: Ruud van Luijk and Anne Postma
At Fox-IT we perform a variety of research and investigation projects to detect malicious activity to improve the service of our Security Operations Center. One of these areas is applying data science techniques to real-world data in real-world production environments, such as anomalous SMB sequences, beaconing patterns, and other unexpected patterns. This blog entry will share an application of machine learning to detect random-like patterns, indicating possible malicious activity.
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