• Feb 07, 2023 News!JACN will adopt Article-by-Article Work Flow. The benefit of article-by-article workflow is that a delay with one article may not delay the entire issue. Once a paper steps into production, it will be published online soon.   [Click]
  • May 30, 2022 News!JACN Vol.10, No.1 has been published with online version.   [Click]
  • Dec 24, 2021 News!Volume 9 No 1 has been indexed by EI (inspec)!   [Click]
General Information
    • ISSN: 1793-8244 (Print)
    • Abbreviated Title:  J. Adv. Comput. Netw.
    • Frequency: Semiyearly
    • DOI: 10.18178/JACN
    • Editor-in-Chief: Professor Haklin Kimm
    • Executive Editor: Ms. Cherry Chan
    • Abstracting/ Indexing: EBSCO, ProQuest, and Google Scholar.
    • E-mail: jacn@ejournal.net
    • APC: 500USD
Professor Haklin Kimm
East Stroudsburg University, USA
I'm happy to take on the position of editor in chief of JACN. We encourage authors to submit papers on all aspects of computer networks.

JACN 2013 Vol.1(3): 260-264 ISSN: 1793-8244
DOI: 10.7763/JACN.2013.V1.52

Malicious Websites Detection and Search Engine Protection

Hao Zhou, Jianhua Sun, and Hao Chen

Abstract—With the development of the Internet, the amount of information is expanding rapidly. Naturally, search engine becomes the backbone of information management. Nevertheless, the flooding of large number of malicious websites on search engine has posed tremendous threat to our users. Most of exiting systems to detect malicious websites focus on specific attack. At the same time, available browser extensions based on blacklist are powerless to countless websites. In this paper, we present a lightweight approach using static analysis techniques to quickly discriminate malicious sites comprising malware, drive-by-download and phishing sites. We extract comprehensive features to classify labeled dataset using various machine learning algorithms. Large scale evaluation of our dataset shows that the classification accuracy reaches 97.5% with low overhead. Furthermore, we achieved a chrome plugin to detect malicious search result websites based on our classification model.

Index Terms—Malicious websites, feature extracting, machine learning.

The authors were with Hunan University, Changsha, CHINA. (e-mail: zhouhao6278@yahoo.com.cn; jhsun@aimlab.org; and haochen@aimlab.org )


Cite:Hao Zhou, Jianhua Sun, and Hao Chen, "Malicious Websites Detection and Search Engine Protection," Journal of Advances in Computer Networks vol. 1, no. 3, pp. 260-264, 2013.

Copyright © 2008-2024. Journal of Advances in Computer Networks.  All rights reserved.
E-mail: jacn@ejournal.net