Detecting malicious domain names using deep learning approaches at scale-Reference-Cited by-同舟云学术

Detecting malicious domain names using deep learning approaches at scale

Published:2018-03-22 Issue:3 Volume:34 Page:1355-1367
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Vinayakumar R.¹,Soman K.P.¹,Poornachandran Prabaharan²

Affiliation:

1. Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India

2. Center for Cyber Security Systems and Networks, Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference31 articles.

1. Sandeep Y. , Reddy R.A.K.K and Ranjan S., Detecting algorithmically generated malicious domain names, in Proceedings of the 10th annual Conference on Internet Measurement, New York, 2010.

2. Antonakakis M. , Perdisci R. , Nadji Y. , Vasiloglou N. , Abu-Nimeh S. , Lee W. and Dagon D. , From throw-away traffic to bots: detecting the rise of DGA-based malware, in P21st USENIX Security Symposium (USENIX Security 12), pp. 491–506, 2012.

3. Detecting algorithmically generated domain-flux attacks with DNS traffic analysis;Yadav;IEEE/Acm Transactions on Networking

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