A Detection Algorithm of Malicious Domain Based on Deep Learning and Multi-Head Attention Mechanism

Siqi Huang, Bo Yan, Dongmei Zhang

2019

Abstract

DGA (domain generation algorithms) domain names are a class of domain names generated by specific algorithms which are used to communicate with malicious C&C servers. DGA based on the PCFG model has been proposed Lately. Under the test of existing DGA detection techniques, its anti-detection effect is very obvious. One of the reasons is that these domains are generated by legal domain names and have the same statistical characteristics of legitimate domain names. This paper proposes a detection model which combines deep learning and Multi-head attention mechanism. It employs these two techniques to extract the features of the domain names. Experiment results show that the model has a good effect on detecting domain names based on PCFG model.

Paper Citation

in Harvard Style

Huang S., Yan B. and Zhang D. (2019). A Detection Algorithm of Malicious Domain Based on Deep Learning and Multi-Head Attention Mechanism.In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC, ISBN 978-989-758-357-5, pages 84-91. DOI: 10.5220/0008098200840091

in Bibtex Style

@conference{ctisc19,
author={Siqi Huang and Bo Yan and Dongmei Zhang},
title={A Detection Algorithm of Malicious Domain Based on Deep Learning and Multi-Head Attention Mechanism},
booktitle={Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,},
year={2019},
pages={84-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008098200840091},
isbn={978-989-758-357-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,
TI - A Detection Algorithm of Malicious Domain Based on Deep Learning and Multi-Head Attention Mechanism
SN - 978-989-758-357-5
AU - Huang S.
AU - Yan B.
AU - Zhang D.
PY - 2019
SP - 84
EP - 91
DO - 10.5220/0008098200840091