Abstract
Malicious domain detection is one of the most effective approaches applied in detecting Advanced Persistent Threat (APT), the most sophisticated and stealthy threat to modern network. Domain name analysis provides security experts with insights to identify the Command and Control (C&C) communications in APT attacks. In this paper, we propose a machine learning based methodology to detect malware domain names by using Extreme Learning Machine (ELM). ELM is a modern neural network with high accuracy and fast learning speed. We apply ELM to classify domain names based on features extracted from multiple resources. Our experiment reveals the introduced detection method is able to perform high detection rate and accuracy (of more than 95%). The fast learning speed of our ELM based approach is also demonstrated by a comparative experiment. Hence, we believe our method using ELM is both effective and efficient to identify malicious domains and therefore enhance the current detection mechanism of APT attacks.
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Shi, Y., Chen, G. & Li, J. Malicious Domain Name Detection Based on Extreme Machine Learning. Neural Process Lett 48, 1347–1357 (2018). https://doi.org/10.1007/s11063-017-9666-7
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DOI: https://doi.org/10.1007/s11063-017-9666-7