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Detection of Malicious Domain Names
Setinský, Jiří ; Perešíni, Martin (referee) ; Tisovčík, Peter (advisor)
The bachelor thesis deals with the detection of artificially generated domain names (DGA). The generated addresses serve as a means of communication between the attacker and the infected computer. By detection, we can detect and track infected computers on the network. The detection itself is preceded by the study of machine learning techniques, which will then be applied in the creation of the detector. To create the final classifier in the form of a decision tree, it was necessary to analyze the principle of DGA addresses. Based on their characteristics, the attributes were extracted, according to which the final classifier will be decided. After learning the classification model on the training set, the classifier was implemented in the target platform NEMEA as a detection module. After final optimizations and testing, we achieved a accuracy of the classifier of 99%, which is a very positive result. The NEMEA module is ready for real-world deployment to detect security incidents. In addition to the NEMEA module, another model was created to predict the accuracy of datasets with domain names. The model is trained based on the characteristics of the dataset and the accuracy of the DGA detector, whose behavior we want to predict.
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Detection of Malicious Domain Names
Setinský, Jiří ; Perešíni, Martin (referee) ; Tisovčík, Peter (advisor)
The bachelor thesis deals with the detection of artificially generated domain names (DGA). The generated addresses serve as a means of communication between the attacker and the infected computer. By detection, we can detect and track infected computers on the network. The detection itself is preceded by the study of machine learning techniques, which will then be applied in the creation of the detector. To create the final classifier in the form of a decision tree, it was necessary to analyze the principle of DGA addresses. Based on their characteristics, the attributes were extracted, according to which the final classifier will be decided. After learning the classification model on the training set, the classifier was implemented in the target platform NEMEA as a detection module. After final optimizations and testing, we achieved a accuracy of the classifier of 99%, which is a very positive result. The NEMEA module is ready for real-world deployment to detect security incidents. In addition to the NEMEA module, another model was created to predict the accuracy of datasets with domain names. The model is trained based on the characteristics of the dataset and the accuracy of the DGA detector, whose behavior we want to predict.
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