Hybrid Phishing URL Detection Using Segmented Word Embedding

Eint Sandi Aung; Hayato Yamana

doi:10.1007/978-3-031-21047-1_46

Hybrid Phishing URL Detection Using Segmented Word Embedding

Eint Sandi Aung^*, Hayato Yamana

^*この研究の対応する著者

基幹理工学部

研究成果: Conference contribution

抄録

Phishing is a type of cybercrime committed by attackers to steal sensitive information. This paper focuses on URL-based phishing detection, i.e., detecting phishing webpages by analyzing the URL. Previously proposed methods tackled this problem; however, insufficient word tokenization of URLs arises unknown words, which degrades the detection accuracy. To solve the unknown-word problem, we propose a new tokenization algorithm, called URL-Tokenizer, which integrates BERT and WordSegment tokenizers, besides utilizing 24 NLP features. Then, we adopt the URL-Tokenizer to the DNN-CNN hybrid model to leverage the detection accuracy. Our experiment using the Ebbu2017 dataset confirmed that our word-DNN-CNN achieves an AUC of 99.89% compared to the state-of-the-art DNN-BiLSTM with an AUC of 98.78%.

本文言語	English
ホスト出版物のタイトル	Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings
編集者	Eric Pardede, Pari Delir Haghighi, Ismail Khalil, Gabriele Kotsis
出版社	Springer Science and Business Media Deutschland GmbH
ページ	507-518
ページ数	12
ISBN（印刷版）	9783031210464
DOI	https://doi.org/10.1007/978-3-031-21047-1_46
出版ステータス	Published - 2022
イベント	24th International Conference on Information Integration and Web Intelligence, iiWAS 2022, held in conjunction with the 20th International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2022 - Virtual, Online 継続期間: 2022 11月 28 → 2022 11月 30

出版物シリーズ

名前	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
巻	13635 LNCS
ISSN（印刷版）	0302-9743
ISSN（電子版）	1611-3349

Conference

Conference	24th International Conference on Information Integration and Web Intelligence, iiWAS 2022, held in conjunction with the 20th International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2022
City	Virtual, Online
Period	22/11/28 → 22/11/30

ASJC Scopus subject areas

理論的コンピュータサイエンス
コンピュータサイエンス（全般）

UN SDG

この成果は、次の持続可能な開発目標に貢献しています

文献へのアクセス

10.1007/978-3-031-21047-1_46

他のファイルとリンク

引用スタイル

Aung, E. S., & Yamana, H. (2022). Hybrid Phishing URL Detection Using Segmented Word Embedding. In E. Pardede, P. Delir Haghighi, I. Khalil, & G. Kotsis (Eds.), Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings (pp. 507-518). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13635 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21047-1_46

Hybrid Phishing URL Detection Using Segmented Word Embedding. / Aung, Eint Sandi; Yamana, Hayato.

Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings. ed. / Eric Pardede; Pari Delir Haghighi; Ismail Khalil; Gabriele Kotsis. Springer Science and Business Media Deutschland GmbH, 2022. p. 507-518 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13635 LNCS).

研究成果: Conference contribution

Aung, ES & Yamana, H 2022, Hybrid Phishing URL Detection Using Segmented Word Embedding. in E Pardede, P Delir Haghighi, I Khalil & G Kotsis (eds), Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13635 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 507-518, 24th International Conference on Information Integration and Web Intelligence, iiWAS 2022, held in conjunction with the 20th International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2022, Virtual, Online, 22/11/28. https://doi.org/10.1007/978-3-031-21047-1_46

Aung ES, Yamana H. Hybrid Phishing URL Detection Using Segmented Word Embedding. In Pardede E, Delir Haghighi P, Khalil I, Kotsis G, editors, Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 507-518. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-21047-1_46

Aung, Eint Sandi ; Yamana, Hayato. / Hybrid Phishing URL Detection Using Segmented Word Embedding. Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings. editor / Eric Pardede ; Pari Delir Haghighi ; Ismail Khalil ; Gabriele Kotsis. Springer Science and Business Media Deutschland GmbH, 2022. pp. 507-518 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d6cb9968d9e6406d8ca6ff46cc991e7b,

title = "Hybrid Phishing URL Detection Using Segmented Word Embedding",

abstract = "Phishing is a type of cybercrime committed by attackers to steal sensitive information. This paper focuses on URL-based phishing detection, i.e., detecting phishing webpages by analyzing the URL. Previously proposed methods tackled this problem; however, insufficient word tokenization of URLs arises unknown words, which degrades the detection accuracy. To solve the unknown-word problem, we propose a new tokenization algorithm, called URL-Tokenizer, which integrates BERT and WordSegment tokenizers, besides utilizing 24 NLP features. Then, we adopt the URL-Tokenizer to the DNN-CNN hybrid model to leverage the detection accuracy. Our experiment using the Ebbu2017 dataset confirmed that our word-DNN-CNN achieves an AUC of 99.89% compared to the state-of-the-art DNN-BiLSTM with an AUC of 98.78%.",

keywords = "Phishing URL detection, Word embedding, Word segmentation",

author = "Aung, {Eint Sandi} and Hayato Yamana",

note = "Funding Information: Acknowledgement. This work was partially supported by JST SPRING Grant Number JPMJSP2128. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 24th International Conference on Information Integration and Web Intelligence, iiWAS 2022, held in conjunction with the 20th International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2022 ; Conference date: 28-11-2022 Through 30-11-2022",

year = "2022",

doi = "10.1007/978-3-031-21047-1_46",

language = "English",

isbn = "9783031210464",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "507--518",

editor = "Eric Pardede and {Delir Haghighi}, Pari and Ismail Khalil and Gabriele Kotsis",

booktitle = "Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings",

}

TY - GEN

T1 - Hybrid Phishing URL Detection Using Segmented Word Embedding

AU - Aung, Eint Sandi

AU - Yamana, Hayato

N1 - Funding Information: Acknowledgement. This work was partially supported by JST SPRING Grant Number JPMJSP2128. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Phishing is a type of cybercrime committed by attackers to steal sensitive information. This paper focuses on URL-based phishing detection, i.e., detecting phishing webpages by analyzing the URL. Previously proposed methods tackled this problem; however, insufficient word tokenization of URLs arises unknown words, which degrades the detection accuracy. To solve the unknown-word problem, we propose a new tokenization algorithm, called URL-Tokenizer, which integrates BERT and WordSegment tokenizers, besides utilizing 24 NLP features. Then, we adopt the URL-Tokenizer to the DNN-CNN hybrid model to leverage the detection accuracy. Our experiment using the Ebbu2017 dataset confirmed that our word-DNN-CNN achieves an AUC of 99.89% compared to the state-of-the-art DNN-BiLSTM with an AUC of 98.78%.

AB - Phishing is a type of cybercrime committed by attackers to steal sensitive information. This paper focuses on URL-based phishing detection, i.e., detecting phishing webpages by analyzing the URL. Previously proposed methods tackled this problem; however, insufficient word tokenization of URLs arises unknown words, which degrades the detection accuracy. To solve the unknown-word problem, we propose a new tokenization algorithm, called URL-Tokenizer, which integrates BERT and WordSegment tokenizers, besides utilizing 24 NLP features. Then, we adopt the URL-Tokenizer to the DNN-CNN hybrid model to leverage the detection accuracy. Our experiment using the Ebbu2017 dataset confirmed that our word-DNN-CNN achieves an AUC of 99.89% compared to the state-of-the-art DNN-BiLSTM with an AUC of 98.78%.

KW - Phishing URL detection

KW - Word embedding

KW - Word segmentation

UR - http://www.scopus.com/inward/record.url?scp=85145009843&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85145009843&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-21047-1_46

DO - 10.1007/978-3-031-21047-1_46

M3 - Conference contribution

AN - SCOPUS:85145009843

SN - 9783031210464

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 507

EP - 518

BT - Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings

A2 - Pardede, Eric

A2 - Delir Haghighi, Pari

A2 - Khalil, Ismail

A2 - Kotsis, Gabriele

PB - Springer Science and Business Media Deutschland GmbH

T2 - 24th International Conference on Information Integration and Web Intelligence, iiWAS 2022, held in conjunction with the 20th International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2022

Y2 - 28 November 2022 through 30 November 2022

ER -

Hybrid Phishing URL Detection Using Segmented Word Embedding

抄録

出版物シリーズ

Conference

ASJC Scopus subject areas

UN SDG

文献へのアクセス

他のファイルとリンク

フィンガープリント

引用スタイル