Study of high-speed malicious Web page detection system based on two-step classifier

In view of the increasing number of new Web pages and the increasing pressure of traditional detection methods,the naive Bayesian algorithm and the support vector machine algorithm were used to design and implement a malicious Web detection system with both efficiency and function,TSMWD ,two-step ma...

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Main Authors: Zheng-qi WANG, Xiao-bing FENG, Chi ZHANG
Format: Article
Language:English
Published: POSTS&TELECOM PRESS Co., LTD 2017-08-01
Series:网络与信息安全学报
Subjects:
Online Access:http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2017.00186
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author Zheng-qi WANG
Xiao-bing FENG
Chi ZHANG
author_facet Zheng-qi WANG
Xiao-bing FENG
Chi ZHANG
author_sort Zheng-qi WANG
collection DOAJ
description In view of the increasing number of new Web pages and the increasing pressure of traditional detection methods,the naive Bayesian algorithm and the support vector machine algorithm were used to design and implement a malicious Web detection system with both efficiency and function,TSMWD ,two-step malicious Web page detection.The first step of detection system was mainly used to filter a large number of normal Web pages,which was characterized by high efficiency,speed,update iteration easy,real rate priority.After the former filter,due to the limited number of samples,the main pursuit of the second step was the detection rate.The experimental results show that the proposed scheme can improve the detection speed of the system under the condition that the overall detection accuracy is basically the same,and can accept more detection requests in certain time.
format Article
id doaj-art-e6428c165a6c409eb881454a3859f619
institution Kabale University
issn 2096-109X
language English
publishDate 2017-08-01
publisher POSTS&TELECOM PRESS Co., LTD
record_format Article
series 网络与信息安全学报
spelling doaj-art-e6428c165a6c409eb881454a3859f6192025-01-15T03:06:00ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2017-08-013446059551265Study of high-speed malicious Web page detection system based on two-step classifierZheng-qi WANGXiao-bing FENGChi ZHANGIn view of the increasing number of new Web pages and the increasing pressure of traditional detection methods,the naive Bayesian algorithm and the support vector machine algorithm were used to design and implement a malicious Web detection system with both efficiency and function,TSMWD ,two-step malicious Web page detection.The first step of detection system was mainly used to filter a large number of normal Web pages,which was characterized by high efficiency,speed,update iteration easy,real rate priority.After the former filter,due to the limited number of samples,the main pursuit of the second step was the detection rate.The experimental results show that the proposed scheme can improve the detection speed of the system under the condition that the overall detection accuracy is basically the same,and can accept more detection requests in certain time.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2017.00186malicious Web page detectionnetwork securitymachine learningfeature extraction
spellingShingle Zheng-qi WANG
Xiao-bing FENG
Chi ZHANG
Study of high-speed malicious Web page detection system based on two-step classifier
网络与信息安全学报
malicious Web page detection
network security
machine learning
feature extraction
title Study of high-speed malicious Web page detection system based on two-step classifier
title_full Study of high-speed malicious Web page detection system based on two-step classifier
title_fullStr Study of high-speed malicious Web page detection system based on two-step classifier
title_full_unstemmed Study of high-speed malicious Web page detection system based on two-step classifier
title_short Study of high-speed malicious Web page detection system based on two-step classifier
title_sort study of high speed malicious web page detection system based on two step classifier
topic malicious Web page detection
network security
machine learning
feature extraction
url http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2017.00186
work_keys_str_mv AT zhengqiwang studyofhighspeedmaliciouswebpagedetectionsystembasedontwostepclassifier
AT xiaobingfeng studyofhighspeedmaliciouswebpagedetectionsystembasedontwostepclassifier
AT chizhang studyofhighspeedmaliciouswebpagedetectionsystembasedontwostepclassifier