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Automated Detection of Malware Activity on Local Networks
Pap, Adam ; Regéciová, Dominika (oponent) ; Ryšavý, Ondřej (vedoucí práce)
The aim of this work is to analyze the network communication of malware and then identify suitable significant features that would allow to develop a suitable method for its detection. As part of the solution of the thesis, a dataset was created from which an IoC for each malware family were extracted. These IoCs were then validated through the AlienVault OTX platform, in order to verify their relevance. Metrics such as false positive rate, accuracy and sensitivity were used for evaluation. On the test data, the two IoC models created from the datasets achieved an accuracy of 99.337% and 94.732% for dataset 1 and 2, respectively. The IoC models of dataset No. 1 falsely classified 3.03% of communication windows as malicious in real communication. IoC models of set No. 2 classified 5.66% as malicious. After the samples of different malware families were run on the machine, the IoC models of set No. 1 classified 7.14% of the windows as malicious. Set No. 2 models classified 15.79%.

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