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Siamese neural network architecture for homoglyph attacks detection

Publication Type : Journal Article

Publisher : ICT Express

Source : ICT Express, Korean Institute of Communications Information Sciences (2019)

Url : https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85066448424&doi=10.1016%2fj.icte.2019.05.002&partnerID=40&md5=f477cf4322113b25e6487ff18f981018

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2019

Abstract : Primarily an adversary uses homoglyph or spoofing attack approach to obfuscate domain name, file name or process names. This approach facilitates to create domain name, file name or process names which look visually homogeneous to legitimate domain name, file name or process names. This paper introduces Siamese neural network architecture which uses the application of recurrent structures with Keras character level embedding to learn the optimal features by considering an input in the form of raw strings. For comparative study, various recurrent structures are used. The performances obtained by recurrent structures are almost closer. However, the proposed method performed well in comparison to the existing methods such as Edit Distance, Visual Edit Distance and Siamese convolutional neural networks. © 2019 The Korean Institute of Communications and Information Sciences (KICS)

Cite this Research Publication : R. Vinayakumar and Dr. Soman K. P., “Siamese neural network architecture for homoglyph attacks detection”, ICT Express, 2019

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