A Machine Learning Based Approach to Detect Malicious Fast Flux Networks
Sathish Kumar, Brian Xu · 2018
The fast flux domain or network is defined as the rapid and repeated changes to host or domain name server resource records, which result in rapid changes in the Internet Protocol (IP) address to which the domain name of an Internet host or name server resolves to. While the FF domains are used for legal uses, they are being increasingly used for malicious purposes. In this work, to address the existing research challenges in the identification of the malicious fast flux networks and domains, we have designed and implemented real-time malicious flux domain detection solution based on machine learning techniques. While the preliminary experiments executed using real-time datasets are promising, we plan to use the deep learning techniques such as reflective neural networks to improve the fast flux domain classification accuracy and performance.