A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms
The effective detection of malicious webpages plays a paramount role in ensuring the Web security on the Internet. However, the detection results of current methods are poor and their efficiency is low, and thus, it is important and challenging to design an efficient detection scheme that can improv...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8542676/ |
| _version_ | 1818618045896065024 |
|---|---|
| author | Jianhua Liu Mengda Xu Xin Wang Shigen Shen Minglu Li |
| author_facet | Jianhua Liu Mengda Xu Xin Wang Shigen Shen Minglu Li |
| author_sort | Jianhua Liu |
| collection | DOAJ |
| description | The effective detection of malicious webpages plays a paramount role in ensuring the Web security on the Internet. However, the detection results of current methods are poor and their efficiency is low, and thus, it is important and challenging to design an efficient detection scheme that can improve the accuracy of classification of malicious webpages. To overcome this challenge, a Markov detection tree scheme is proposed in this paper to automatically identify and classify malicious webpages, where the link relations of unified resource locators, the information gain ratio, and Markov decision process as well as decision tree are used to analyze malicious webpages simultaneously. To increase the detection accuracy for malicious webpages, two methods of filling missing values are presented to process the null attribute values of webpages. We compare the performance of our algorithms when the different methods are applied in terms of the information gain ratio, classification accuracy, and detection efficiency. Our experimental results show that the proposed methods can improve the accuracy and efficiency in the classification of malicious webpage detections. |
| first_indexed | 2024-12-16T17:15:21Z |
| format | Article |
| id | doaj.art-aa670174121043b89ee0fb1941c4db43 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:15:21Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-aa670174121043b89ee0fb1941c4db432022-12-21T22:23:18ZengIEEEIEEE Access2169-35362018-01-016740257403810.1109/ACCESS.2018.28827428542676A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud PlatformsJianhua Liu0https://orcid.org/0000-0002-9971-4964Mengda Xu1Xin Wang2Shigen Shen3https://orcid.org/0000-0002-7558-5379Minglu Li4Department of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, ChinaDepartment of Electrical and Computer Engineering, The State University of New York at Stony Brook, NY, USADepartment of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaDepartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, ChinaThe effective detection of malicious webpages plays a paramount role in ensuring the Web security on the Internet. However, the detection results of current methods are poor and their efficiency is low, and thus, it is important and challenging to design an efficient detection scheme that can improve the accuracy of classification of malicious webpages. To overcome this challenge, a Markov detection tree scheme is proposed in this paper to automatically identify and classify malicious webpages, where the link relations of unified resource locators, the information gain ratio, and Markov decision process as well as decision tree are used to analyze malicious webpages simultaneously. To increase the detection accuracy for malicious webpages, two methods of filling missing values are presented to process the null attribute values of webpages. We compare the performance of our algorithms when the different methods are applied in terms of the information gain ratio, classification accuracy, and detection efficiency. Our experimental results show that the proposed methods can improve the accuracy and efficiency in the classification of malicious webpage detections.https://ieeexplore.ieee.org/document/8542676/Decision treeMarkov decision processmalicious web detectionmachine learning |
| spellingShingle | Jianhua Liu Mengda Xu Xin Wang Shigen Shen Minglu Li A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms IEEE Access Decision tree Markov decision process malicious web detection machine learning |
| title | A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms |
| title_full | A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms |
| title_fullStr | A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms |
| title_full_unstemmed | A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms |
| title_short | A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms |
| title_sort | markov detection tree based centralized scheme to automatically identify malicious webpages on cloud platforms |
| topic | Decision tree Markov decision process malicious web detection machine learning |
| url | https://ieeexplore.ieee.org/document/8542676/ |
| work_keys_str_mv | AT jianhualiu amarkovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT mengdaxu amarkovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT xinwang amarkovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT shigenshen amarkovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT mingluli amarkovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT jianhualiu markovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT mengdaxu markovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT xinwang markovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT shigenshen markovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms AT mingluli markovdetectiontreebasedcentralizedschemetoautomaticallyidentifymaliciouswebpagesoncloudplatforms |