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Detekce malware domén pomocí metod strojového učení
Ebert, Tomáš ; Poliakov, Daniel (referee) ; Hranický, Radek (advisor)
This bachelor thesis deals with the detection of malware domains using machine learning methods learning based on various information obtained about the domain (DNS records, geolocation data etc.). With the rapid proliferation of threats, not only in the form of malware, the current examples are often approaches are insufficient, either in terms of the speed of detection of malware domains or in terms of overall recognition,whether a domain is dangerous. The output of this work is a trained XGBoost classifier model, which has the advantage of fast and efficient real-time detection over blacklist detection, which often acquires domain data with a week delay. For this model, 131,000 malware domains were obtained, using which obtain a high-value model. Using experiments, a score of F1 of 96.8786 % for the XGBoost classifier with a false positive detection rate of 0.004887.
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