Malicious Domain Detection Based on Decision Tree

Thin Tharaphe THEIN; Yoshiaki SHIRAISHI; Masakatu MORII

doi:10.1587/transinf.2022OFL0002

Malicious Domain Detection Based on Decision Tree

Thin Tharaphe THEIN, Yoshiaki SHIRAISHI, Masakatu MORII

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Summary :

Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.

Publication: IEICE TRANSACTIONS on Information Vol.E106-D No.9 pp.1490-1494

Publication Date: 2023/09/01

Publicized: 2023/06/22

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2022OFL0002

Type of Manuscript: Special Section LETTER (Special Section on Log Data Usage Technology and Office Information Systems)

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Authors

Thin Tharaphe THEIN
  Kobe University
Yoshiaki SHIRAISHI
  Kobe University
Masakatu MORII
  Kobe University

Keyword

malicious domain detection, machine learning, domain name system (DNS)

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Thin Tharaphe THEIN, Yoshiaki SHIRAISHI, Masakatu MORII, "Malicious Domain Detection Based on Decision Tree" in IEICE TRANSACTIONS on Information, vol. E106-D, no. 9, pp. 1490-1494, September 2023, doi: 10.1587/transinf.2022OFL0002.
Abstract: Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022OFL0002/_p

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@ARTICLE{e106-d_9_1490,
author={Thin Tharaphe THEIN, Yoshiaki SHIRAISHI, Masakatu MORII, },
journal={IEICE TRANSACTIONS on Information},
title={Malicious Domain Detection Based on Decision Tree},
year={2023},
volume={E106-D},
number={9},
pages={1490-1494},
abstract={Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.},
keywords={},
doi={10.1587/transinf.2022OFL0002},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - Malicious Domain Detection Based on Decision Tree
T2 - IEICE TRANSACTIONS on Information
SP - 1490
EP - 1494
AU - Thin Tharaphe THEIN
AU - Yoshiaki SHIRAISHI
AU - Masakatu MORII
PY - 2023
DO - 10.1587/transinf.2022OFL0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2023
AB - Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.
ER -