Abstract
Cybercrime has expanded in various areas as the number of websites and hosting services has grown significantly. The identification of hostile domain names has recently gained the interest of researchers in order to avoid cyber-attacks. In terms of network traffic and content, several attempts have been made to distinguish these names. The novel approach proposed in this paper utilizes the neural network-based algorithms to recognize the potentially hostile domain names. An LSTM network is created and prepared based on the dataset. The primary task is to first divide the URL into subdomain, domain, and domain-suffix. Then based on this, the proposed neural network is trained to classify the given data set as malicious or benign. The proposed system can perform well with a higher level of accuracy on the validation set.
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Aarthi, B., Jeenath Shafana, N., Flavia, J., Chelliah, B.J. (2022). A Hybrid Multiclass Classifier Approach for the Detection of Malicious Domain Names Using RNN Model. In: Smys, S., Tavares, J.M.R.S., Balas, V.E. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1420. Springer, Singapore. https://doi.org/10.1007/978-981-16-9573-5_35
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DOI: https://doi.org/10.1007/978-981-16-9573-5_35
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