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Botnets represent one of the most destructive cybersecurity threats. Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection. In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances. This paper investigates the capability of the Self Organizing Map (SOM), an unsupervised learning technique as a data analytics system. In doingdoi:10.13052/jcsm2245-1439.812 fatcat:jvutnccf75fb5ls6osvsmn7jb4