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Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks

Publication Type : Book Chapter

Publisher : Springer International Publishing

Source : Deep Learning Applications for Cyber Security, Springer International Publishing, Cham, p.151–173 (2019)

Url :

ISBN : 9783030130572

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2019

Abstract : In recent years, modern botnets employ the technique of domain generation algorithm (DGA) to evade detection solutions that use either reverse engineering methods, or blacklisting of malicious domain names. DGA facilitates generation of large number of pseudo random domain names to connect to the command and control server. This makes DGAs very convincing for botnet operators (botmasters) to make their botnets more effective and resilient to blacklisting and efforts of shutting-down attacks. Detecting the malicious domains generated by the DGAs in real time is the most challenging task and significant research has been carried out by applying different machine learning algorithms. This research considers contemporary state-of-the-art DGA malicious detection approaches and proposes a deep learning architecture for detecting the DGA generated domain names.

Cite this Research Publication : A. Dinesh Kumar, Thodupunoori, H., Vinayakumar, R., Dr. Soman K. P., Poornachandran, P., Alazab, M., and Venkatraman, S., “Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks”, in Deep Learning Applications for Cyber Security, M. Alazab and Tang, M. J., Eds. Cham: Springer International Publishing, 2019, pp. 151–173.

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