Hacker intrusion detection based on text classification with word2vec model and convolutional neural network

Tongbo Wang; Kaoru Hirota; Chaojun Wang; Dongyun Kim; Yaping Dai

Hacker intrusion detection based on text classification with word2vec model and convolutional neural network

Tongbo Wang, Kaoru Hirota^*, Chaojun Wang, Dongyun Kim, Yaping Dai

^*此作品的通讯作者

自动化学院

Beijing Institute of Technology

科研成果: 会议稿件 › 论文 › 同行评审

摘要

A hacker intrusion detection method is proposed to tighten up Windows system security with text classification, which the Word2vec model and Convolutional Neural Network (CNN) are applied to distinguish hostile intrusion logs from a benign. The proposal makes full use of users’ operation commands contained in Windows system logs to analyze possible intrusion information and it supplies an application of text classification with CNN and word2vec on network security. For experiment, the dataset from a company having thousands of employees is converted to word vectors by word2vec model. The results of CNN with word embedding and classifier Support Vector Machine (SVM) with term frequency-inverse document frequency (TF-IDF) show the intrusion detection rates 95.56% and 87.55%, respectively. The proposed method can be integrated into antivirus software for hacker intrusion detection.

源语言	英语
出版状态	已出版 - 2018
活动	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 - Tengzhou, Shandong, 中国期限: 2 11月 2018 → 6 11月 2018

会议

会议	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018
国家/地区	中国
市	Tengzhou, Shandong
时期	2/11/18 → 6/11/18

其它文件与链接

链接到 Scopus 的出版物

引用此

Wang, T., Hirota, K., Wang, C., Kim, D., & Dai, Y. (2018). Hacker intrusion detection based on text classification with word2vec model and convolutional neural network. 论文发表于 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, 中国.

Wang, Tongbo ; Hirota, Kaoru ; Wang, Chaojun 等. / Hacker intrusion detection based on text classification with word2vec model and convolutional neural network. 论文发表于 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, 中国.

@conference{cc212f310e9c4c7a9534db24b5c326bb,

title = "Hacker intrusion detection based on text classification with word2vec model and convolutional neural network",

abstract = "A hacker intrusion detection method is proposed to tighten up Windows system security with text classification, which the Word2vec model and Convolutional Neural Network (CNN) are applied to distinguish hostile intrusion logs from a benign. The proposal makes full use of users{\textquoteright} operation commands contained in Windows system logs to analyze possible intrusion information and it supplies an application of text classification with CNN and word2vec on network security. For experiment, the dataset from a company having thousands of employees is converted to word vectors by word2vec model. The results of CNN with word embedding and classifier Support Vector Machine (SVM) with term frequency-inverse document frequency (TF-IDF) show the intrusion detection rates 95.56% and 87.55%, respectively. The proposed method can be integrated into antivirus software for hacker intrusion detection.",

keywords = "Convolutional Neural Network, Hacker Intrusion Detection, Support Vector Machine, Text Classification, Word2vec",

author = "Tongbo Wang and Kaoru Hirota and Chaojun Wang and Dongyun Kim and Yaping Dai",

note = "Publisher Copyright: {\textcopyright} ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.; 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 ; Conference date: 02-11-2018 Through 06-11-2018",

year = "2018",

language = "English",

}

Wang, T, Hirota, K, Wang, C, Kim, D & Dai, Y 2018, 'Hacker intrusion detection based on text classification with word2vec model and convolutional neural network', 论文发表于 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, 中国, 2/11/18 - 6/11/18.

Hacker intrusion detection based on text classification with word2vec model and convolutional neural network. / Wang, Tongbo; Hirota, Kaoru; Wang, Chaojun 等.
2018. 论文发表于 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, 中国.

科研成果: 会议稿件 › 论文 › 同行评审

TY - CONF

T1 - Hacker intrusion detection based on text classification with word2vec model and convolutional neural network

AU - Wang, Tongbo

AU - Hirota, Kaoru

AU - Wang, Chaojun

AU - Kim, Dongyun

AU - Dai, Yaping

N1 - Publisher Copyright: © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.

PY - 2018

Y1 - 2018

N2 - A hacker intrusion detection method is proposed to tighten up Windows system security with text classification, which the Word2vec model and Convolutional Neural Network (CNN) are applied to distinguish hostile intrusion logs from a benign. The proposal makes full use of users’ operation commands contained in Windows system logs to analyze possible intrusion information and it supplies an application of text classification with CNN and word2vec on network security. For experiment, the dataset from a company having thousands of employees is converted to word vectors by word2vec model. The results of CNN with word embedding and classifier Support Vector Machine (SVM) with term frequency-inverse document frequency (TF-IDF) show the intrusion detection rates 95.56% and 87.55%, respectively. The proposed method can be integrated into antivirus software for hacker intrusion detection.

AB - A hacker intrusion detection method is proposed to tighten up Windows system security with text classification, which the Word2vec model and Convolutional Neural Network (CNN) are applied to distinguish hostile intrusion logs from a benign. The proposal makes full use of users’ operation commands contained in Windows system logs to analyze possible intrusion information and it supplies an application of text classification with CNN and word2vec on network security. For experiment, the dataset from a company having thousands of employees is converted to word vectors by word2vec model. The results of CNN with word embedding and classifier Support Vector Machine (SVM) with term frequency-inverse document frequency (TF-IDF) show the intrusion detection rates 95.56% and 87.55%, respectively. The proposed method can be integrated into antivirus software for hacker intrusion detection.

KW - Convolutional Neural Network

KW - Hacker Intrusion Detection

KW - Support Vector Machine

KW - Text Classification

KW - Word2vec

UR - http://www.scopus.com/inward/record.url?scp=85057964568&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85057964568

T2 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018

Y2 - 2 November 2018 through 6 November 2018

ER -

Wang T, Hirota K, Wang C, Kim D, Dai Y. Hacker intrusion detection based on text classification with word2vec model and convolutional neural network. 2018. 论文发表于 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, 中国.

Hacker intrusion detection based on text classification with word2vec model and convolutional neural network

摘要

会议

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引用此