An efficient malicious webpage static detection framework based on optimized Bayesian and hybrid machine learning-Reference-Cited by-同舟云学术

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An efficient malicious webpage static detection framework based on optimized Bayesian and hybrid machine learning

Published:2021-12-27 Issue: Volume: Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Yang Fan¹^ORCID,Zhu Chaoqun¹,Xu Heng¹,Qian Yongfeng¹,Song Jun¹^ORCID

Affiliation:

1. School of Computer Science China University of Geosciences Wuhan China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6792

Reference44 articles.

1. Digital 2020 ‐ global digital overview; 2020. Accessed July 15 2021.https://wearesocial.com/special‐reports/digital‐in‐2020‐global‐overview%

2. K. Security LabKaspersky security bulletin; 2020. Accessed July 15 2021.https://securelist.com/kaspersky‐security‐bulletin‐2020‐statistics/.

3. MasriR AldwairiM. Automated malicious advertisement detection using VirusTotal URLVoid and TrendMicro. Proceedings of the 2017 8th International Conference on Information and Communication Systems (ICICS); 2017:336‐341; Irbid Jordan.

4. BellS KomisarczukP. An analysis of phishing blacklists: Google safe browsing OpenPhish and PhishTank. Proceedings of the Australasian Computer Science Week ACSW 2020; Melbourne VIC Australia February 2020.

5. AMA: Static Code Analysis of Web Page for the Detection of Malicious Scripts

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