Research of recognition system of web intrusion detection based on storm

Li Bo; Wang Jinzhen; Zhao Ping; Yan Zhongjiang; Yang Mao

doi:10.1145/3033288.3033319

Research of recognition system of web intrusion detection based on storm

Li Bo, Wang Jinzhen, Zhao Ping, Yan Zhongjiang, Yang Mao

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Based on Storm, a distributed, reliable, fault-tolerant real-time data stream processing system, we propose a recognition system of web intrusion detection. The system is based on machine learning, feature selection algorithm by TF-IDF(Term Frequency Inverse Document Frequency) and the optimised cosine similarity algorithm, at low false positive rate and a higher detection rate of attacks and malicious behavior in real-time to protect the security of user data. From comparative analysis of experiments we find that the system for intrusion recognition rate and false positive rate has improved to some extent, it can be better to complete the intrusion detection work.

Original language	English
Title of host publication	Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016
Publisher	Association for Computing Machinery
Pages	98-102
Number of pages	5
ISBN (Electronic)	9781450347938
DOIs	https://doi.org/10.1145/3033288.3033319
State	Published - 17 Dec 2016
Event	5th International Conference on Network, Communication and Computing, ICNCC 2016 - Kyoto, Japan Duration: 17 Dec 2016 → 21 Dec 2016

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th International Conference on Network, Communication and Computing, ICNCC 2016
Country/Territory	Japan
City	Kyoto
Period	17/12/16 → 21/12/16

Keywords

Big Data
Cosine similarity
Strom
TF-IDF
Web Intrusion Detection System

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10.1145/3033288.3033319

Cite this

Bo, L., Jinzhen, W., Ping, Z., Zhongjiang, Y., & Mao, Y. (2016). Research of recognition system of web intrusion detection based on storm. In Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016 (pp. 98-102). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3033288.3033319

@inproceedings{95d5a43b677a41eb87300c125d6dee85,

title = "Research of recognition system of web intrusion detection based on storm",

abstract = "Based on Storm, a distributed, reliable, fault-tolerant real-time data stream processing system, we propose a recognition system of web intrusion detection. The system is based on machine learning, feature selection algorithm by TF-IDF(Term Frequency Inverse Document Frequency) and the optimised cosine similarity algorithm, at low false positive rate and a higher detection rate of attacks and malicious behavior in real-time to protect the security of user data. From comparative analysis of experiments we find that the system for intrusion recognition rate and false positive rate has improved to some extent, it can be better to complete the intrusion detection work.",

keywords = "Big Data, Cosine similarity, Strom, TF-IDF, Web Intrusion Detection System",

author = "Li Bo and Wang Jinzhen and Zhao Ping and Yan Zhongjiang and Yang Mao",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 5th International Conference on Network, Communication and Computing, ICNCC 2016 ; Conference date: 17-12-2016 Through 21-12-2016",

year = "2016",

month = dec,

day = "17",

doi = "10.1145/3033288.3033319",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

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booktitle = "Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016",

}

Bo, L, Jinzhen, W, Ping, Z, Zhongjiang, Y & Mao, Y 2016, Research of recognition system of web intrusion detection based on storm. in Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 98-102, 5th International Conference on Network, Communication and Computing, ICNCC 2016, Kyoto, Japan, 17/12/16. https://doi.org/10.1145/3033288.3033319

Research of recognition system of web intrusion detection based on storm. / Bo, Li; Jinzhen, Wang; Ping, Zhao et al.
Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016. Association for Computing Machinery, 2016. p. 98-102 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research of recognition system of web intrusion detection based on storm

AU - Bo, Li

AU - Jinzhen, Wang

AU - Ping, Zhao

AU - Zhongjiang, Yan

AU - Mao, Yang

PY - 2016/12/17

Y1 - 2016/12/17

N2 - Based on Storm, a distributed, reliable, fault-tolerant real-time data stream processing system, we propose a recognition system of web intrusion detection. The system is based on machine learning, feature selection algorithm by TF-IDF(Term Frequency Inverse Document Frequency) and the optimised cosine similarity algorithm, at low false positive rate and a higher detection rate of attacks and malicious behavior in real-time to protect the security of user data. From comparative analysis of experiments we find that the system for intrusion recognition rate and false positive rate has improved to some extent, it can be better to complete the intrusion detection work.

AB - Based on Storm, a distributed, reliable, fault-tolerant real-time data stream processing system, we propose a recognition system of web intrusion detection. The system is based on machine learning, feature selection algorithm by TF-IDF(Term Frequency Inverse Document Frequency) and the optimised cosine similarity algorithm, at low false positive rate and a higher detection rate of attacks and malicious behavior in real-time to protect the security of user data. From comparative analysis of experiments we find that the system for intrusion recognition rate and false positive rate has improved to some extent, it can be better to complete the intrusion detection work.

KW - Big Data

KW - Cosine similarity

KW - Strom

KW - TF-IDF

KW - Web Intrusion Detection System

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M3 - 会议稿件

AN - SCOPUS:85018297050

T3 - ACM International Conference Proceeding Series

SP - 98

EP - 102

BT - Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016

PB - Association for Computing Machinery

T2 - 5th International Conference on Network, Communication and Computing, ICNCC 2016

Y2 - 17 December 2016 through 21 December 2016

ER -

Research of recognition system of web intrusion detection based on storm

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