Available Website Names Classification Using Naive Baye

Kanokphon Kane; Khwunta Kirimasthong; Tossapon Boongoen

doi:10.1007/978-3-031-47508-521

Available Website Names Classification Using Naive Baye

Kanokphon Kane, Khwunta Kirimasthong, Tossapon Boongoen

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

Abstract

This paper presents a method for classifying website names using machine learning techniques based on the analysis of URLs from different websites on the internet. The primary objective is to categorize websites as either positive or negative, aiding in access permissions. The proposed method offers advantages such as improved content filtering, increased risk awareness, enhanced access control, and a comparative analysis with Decision Tree and Logistic Regression models. The experimental dataset includes training and testing data of website URLs, along with external datasets for sentiment analysis. The results demonstrate an impressive accuracy rate of 94 validating the suitability of the method for website name classification. Future work can explore the application of the classification method in network security to detect and block negative websites by classifying them as malicious URLs. This extension would further enhance protection against harmful content and contribute to a more secure online environment.

Original language	English
Title of host publication	ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2023
Editors	P Jenkins, P Grace, L Yang, S Prajapat, N Naik
Place of Publication	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Publisher	Springer Nature
Pages	259-269
Number of pages	11
Volume	1453
ISBN (Print)	978-3-031-47507-8; 978-3-031-47508-5
DOIs	https://doi.org/10.1007/978-3-031-47508-521
Publication status	Published - 2024

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	SPRINGER INTERNATIONAL PUBLISHING AG

Keywords

Multinomial naive bayes
Website name classification
Machine learning
Malicious URL
Network security

Access to Document

10.1007/978-3-031-47508-521

Permanent link

Persistent link

Cite this

Kane, Kanokphon ; Kirimasthong, Khwunta ; Boongoen, Tossapon. / Available Website Names Classification Using Naive Baye. ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2023. editor / P Jenkins ; P Grace ; L Yang ; S Prajapat ; N Naik. Vol. 1453 GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Nature, 2024. pp. 259-269 (Advances in Intelligent Systems and Computing).

@inproceedings{4be12ab3570547048106824e80651d38,

title = "Available Website Names Classification Using Naive Baye",

abstract = "This paper presents a method for classifying website names using machine learning techniques based on the analysis of URLs from different websites on the internet. The primary objective is to categorize websites as either positive or negative, aiding in access permissions. The proposed method offers advantages such as improved content filtering, increased risk awareness, enhanced access control, and a comparative analysis with Decision Tree and Logistic Regression models. The experimental dataset includes training and testing data of website URLs, along with external datasets for sentiment analysis. The results demonstrate an impressive accuracy rate of 94 validating the suitability of the method for website name classification. Future work can explore the application of the classification method in network security to detect and block negative websites by classifying them as malicious URLs. This extension would further enhance protection against harmful content and contribute to a more secure online environment.",

keywords = "Multinomial naive bayes, Website name classification, Machine learning, Malicious URL, Network security",

author = "Kanokphon Kane and Khwunta Kirimasthong and Tossapon Boongoen",

note = "22nd UK Workshop on Computational Intelligence (UKCI), Birmingham, ENGLAND, SEP 06-08, 2023",

year = "2024",

doi = "10.1007/978-3-031-47508-521",

language = "English",

isbn = "978-3-031-47507-8; 978-3-031-47508-5",

volume = "1453",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Nature",

pages = "259--269",

editor = "P Jenkins and P Grace and L Yang and S Prajapat and N Naik",

booktitle = "ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2023",

address = "Switzerland",

}

Kane, K, Kirimasthong, K & Boongoen, T 2024, Available Website Names Classification Using Naive Baye. in P Jenkins, P Grace, L Yang, S Prajapat & N Naik (eds), ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2023. vol. 1453, Advances in Intelligent Systems and Computing, Springer Nature, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, pp. 259-269. https://doi.org/10.1007/978-3-031-47508-521

Available Website Names Classification Using Naive Baye. / Kane, Kanokphon; Kirimasthong, Khwunta; Boongoen, Tossapon.
ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2023. ed. / P Jenkins; P Grace; L Yang; S Prajapat; N Naik. Vol. 1453 GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND: Springer Nature, 2024. p. 259-269 (Advances in Intelligent Systems and Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

TY - GEN

T1 - Available Website Names Classification Using Naive Baye

AU - Kane, Kanokphon

AU - Kirimasthong, Khwunta

AU - Boongoen, Tossapon

N1 - 22nd UK Workshop on Computational Intelligence (UKCI), Birmingham, ENGLAND, SEP 06-08, 2023

PY - 2024

Y1 - 2024

N2 - This paper presents a method for classifying website names using machine learning techniques based on the analysis of URLs from different websites on the internet. The primary objective is to categorize websites as either positive or negative, aiding in access permissions. The proposed method offers advantages such as improved content filtering, increased risk awareness, enhanced access control, and a comparative analysis with Decision Tree and Logistic Regression models. The experimental dataset includes training and testing data of website URLs, along with external datasets for sentiment analysis. The results demonstrate an impressive accuracy rate of 94 validating the suitability of the method for website name classification. Future work can explore the application of the classification method in network security to detect and block negative websites by classifying them as malicious URLs. This extension would further enhance protection against harmful content and contribute to a more secure online environment.

AB - This paper presents a method for classifying website names using machine learning techniques based on the analysis of URLs from different websites on the internet. The primary objective is to categorize websites as either positive or negative, aiding in access permissions. The proposed method offers advantages such as improved content filtering, increased risk awareness, enhanced access control, and a comparative analysis with Decision Tree and Logistic Regression models. The experimental dataset includes training and testing data of website URLs, along with external datasets for sentiment analysis. The results demonstrate an impressive accuracy rate of 94 validating the suitability of the method for website name classification. Future work can explore the application of the classification method in network security to detect and block negative websites by classifying them as malicious URLs. This extension would further enhance protection against harmful content and contribute to a more secure online environment.

KW - Multinomial naive bayes

KW - Website name classification

KW - Machine learning

KW - Malicious URL

KW - Network security

U2 - 10.1007/978-3-031-47508-521

DO - 10.1007/978-3-031-47508-521

M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-031-47507-8; 978-3-031-47508-5

VL - 1453

T3 - Advances in Intelligent Systems and Computing

SP - 259

EP - 269

BT - ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2023

A2 - Jenkins, P

A2 - Grace, P

A2 - Yang, L

A2 - Prajapat, S

A2 - Naik, N

PB - Springer Nature

CY - GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

ER -

Kane K, Kirimasthong K, Boongoen T. Available Website Names Classification Using Naive Baye. In Jenkins P, Grace P, Yang L, Prajapat S, Naik N, editors, ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2023. Vol. 1453. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND: Springer Nature. 2024. p. 259-269. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-031-47508-521

Available Website Names Classification Using Naive Baye

Abstract

Publication series

Keywords

Access to Document

Permanent link

Fingerprint

Cite this