Using a Machine Learning Model for Malicious URL Type Detection

Suet Ping Tung; Ka Yan Wong; Ievgeniia Kuzminykh; Taimur Bakhshi; Bogdan Ghita

doi:10.1007/978-3-030-97777-1_41

Using a Machine Learning Model for Malicious URL Type Detection

Suet Ping Tung, Ka Yan Wong, Ievgeniia Kuzminykh, Taimur Bakhshi, Bogdan Ghita

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The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.

Iaith wreiddiol	Saesneg
Teitl	Internet of Things, Smart Spaces, and Next Generation Networks and Systems
Is-deitl	21st International Conference, NEW2AN 2021, and 14th Conference, ruSMART 2021, St. Petersburg, Russia, August 26–27, 2021, Proceedings
Golygyddion	Yevgeni Koucheryavy, Sergey Balandin, Sergey Andreev
Cyhoeddwr	Springer
Tudalennau	493-505
Nifer y tudalennau	13
ISBN (Electronig)	978-3-030-97777-1
ISBN (Argraffiad)	978-3-030-97776-4
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-030-97777-1_41
Statws	Cyhoeddwyd - 16 Maw 2022
Cyhoeddwyd yn allanol	Ie
Digwyddiad	21st International Conference on Next Generation Wired/Wireless Networks and Systems - Online, St, Petersburg, Rwsia Hyd: 30 Awst 2021 → 31 Awst 2021

Cyfres gyhoeddiadau

Enw	Lecture Notes in Computer Science
Cyhoeddwr	Springer
Cyfrol	13158
ISSN (Argraffiad)	0302-9743
ISSN (Electronig)	1611-3349

Cynhadledd

Cynhadledd	21st International Conference on Next Generation Wired/Wireless Networks and Systems
Teitl cryno	NEW2AN 2021
Gwlad/Tiriogaeth	Rwsia
Dinas	St, Petersburg
Cyfnod	30/08/21 → 31/08/21

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10.1007/978-3-030-97777-1_41

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Tung, S. P., Wong, K. Y., Kuzminykh, I., Bakhshi, T., & Ghita, B. (2022). Using a Machine Learning Model for Malicious URL Type Detection. Yn Y. Koucheryavy, S. Balandin, & S. Andreev (Gol.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 21st International Conference, NEW2AN 2021, and 14th Conference, ruSMART 2021, St. Petersburg, Russia, August 26–27, 2021, Proceedings (tt. 493-505). (Lecture Notes in Computer Science; Cyfrol 13158). Springer. https://doi.org/10.1007/978-3-030-97777-1_41

Tung, Suet Ping ; Wong, Ka Yan ; Kuzminykh, Ievgeniia et al. / Using a Machine Learning Model for Malicious URL Type Detection. Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 21st International Conference, NEW2AN 2021, and 14th Conference, ruSMART 2021, St. Petersburg, Russia, August 26–27, 2021, Proceedings. Gol. / Yevgeni Koucheryavy ; Sergey Balandin ; Sergey Andreev. Springer, 2022. tt. 493-505 (Lecture Notes in Computer Science).

@inproceedings{db8d98ee495248b6839f7abbf264c84f,

title = "Using a Machine Learning Model for Malicious URL Type Detection",

abstract = "The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.",

keywords = "Malicious URL, Web security, Machine Learning, Phishing, Spamming, Malware, Lexical Feature, Traffic",

author = "Tung, {Suet Ping} and Wong, {Ka Yan} and Ievgeniia Kuzminykh and Taimur Bakhshi and Bogdan Ghita",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 21st International Conference on Next Generation Wired/Wireless Networks and Systems, NEW2AN 2021 ; Conference date: 30-08-2021 Through 31-08-2021",

year = "2022",

month = mar,

day = "16",

doi = "10.1007/978-3-030-97777-1_41",

language = "English",

isbn = "978-3-030-97776-4",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "493--505",

editor = "Yevgeni Koucheryavy and Sergey Balandin and Sergey Andreev",

booktitle = "Internet of Things, Smart Spaces, and Next Generation Networks and Systems",

address = "Germany",

}

Tung, SP, Wong, KY, Kuzminykh, I, Bakhshi, T & Ghita, B 2022, Using a Machine Learning Model for Malicious URL Type Detection. yn Y Koucheryavy, S Balandin & S Andreev (gol.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 21st International Conference, NEW2AN 2021, and 14th Conference, ruSMART 2021, St. Petersburg, Russia, August 26–27, 2021, Proceedings. Lecture Notes in Computer Science, cyfrol. 13158, Springer, tt. 493-505, 21st International Conference on Next Generation Wired/Wireless Networks and Systems, St, Petersburg, Rwsia, 30/08/21. https://doi.org/10.1007/978-3-030-97777-1_41

Using a Machine Learning Model for Malicious URL Type Detection. / Tung, Suet Ping; Wong, Ka Yan; Kuzminykh, Ievgeniia et al.
Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 21st International Conference, NEW2AN 2021, and 14th Conference, ruSMART 2021, St. Petersburg, Russia, August 26–27, 2021, Proceedings. gol. / Yevgeni Koucheryavy; Sergey Balandin; Sergey Andreev. Springer, 2022. t. 493-505 (Lecture Notes in Computer Science; Cyfrol 13158).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i gynhadledd › adolygiad gan gymheiriaid

TY - GEN

T1 - Using a Machine Learning Model for Malicious URL Type Detection

AU - Tung, Suet Ping

AU - Wong, Ka Yan

AU - Kuzminykh, Ievgeniia

AU - Bakhshi, Taimur

AU - Ghita, Bogdan

PY - 2022/3/16

Y1 - 2022/3/16

N2 - The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.

AB - The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.

KW - Malicious URL

KW - Web security

KW - Machine Learning

KW - Phishing

KW - Spamming

KW - Malware

KW - Lexical Feature

KW - Traffic

U2 - 10.1007/978-3-030-97777-1_41

DO - 10.1007/978-3-030-97777-1_41

M3 - Conference contribution

SN - 978-3-030-97776-4

T3 - Lecture Notes in Computer Science

SP - 493

EP - 505

BT - Internet of Things, Smart Spaces, and Next Generation Networks and Systems

A2 - Koucheryavy, Yevgeni

A2 - Balandin, Sergey

A2 - Andreev, Sergey

PB - Springer

T2 - 21st International Conference on Next Generation Wired/Wireless Networks and Systems

Y2 - 30 August 2021 through 31 August 2021

ER -

Tung SP, Wong KY, Kuzminykh I, Bakhshi T, Ghita B. Using a Machine Learning Model for Malicious URL Type Detection. Yn Koucheryavy Y, Balandin S, Andreev S, golygyddion, Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 21st International Conference, NEW2AN 2021, and 14th Conference, ruSMART 2021, St. Petersburg, Russia, August 26–27, 2021, Proceedings. Springer. 2022. t. 493-505. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-97777-1_41

Using a Machine Learning Model for Malicious URL Type Detection

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