Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm

Murat Koca; İsa Avcı; Mohammed Abdulkareem Shakir Al-hayani

doi:10.35377/saucis...1273536

EN

Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm

Abstract

The financial losses of vulnerable and insecure websites are increasing day by day. The proposed system in this research presents a strategy based on factor analysis of website categories and accurate identification of unknown information to classify safe and dangerous websites and protect users from the previous one. Probability calculations based on Naive Bayes and other powerful approaches are used throughout the website classification procedure to evaluate and train the website classification model. According to our study, the Naive Bayes approach was benign and showed successful results compared to other tests. This strategy is best optimized to solve the problem of distinguishing secure websites from unsafe ones. The vulnerability data categorization training model included in this datasheet had a better degree of precision. In this study, the best accuracy probability of 96% was achieved in Naive Bayes' NSL-KDD data set categorization

Keywords

References

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Murat Koca ^*
0000-0002-6048-7645
Türkiye

İsa Avcı
0000-0001-7032-8018
Türkiye

Mohammed Abdulkareem Shakir Al-hayani
0009-0002-3847-9603
Türkiye

Early Pub Date

August 27, 2023

Publication Date

August 31, 2023

Submission Date

March 30, 2023

Acceptance Date

May 27, 2023

Published in Issue

Year 2023 Volume: 6 Number: 2

DOI

https://doi.org/10.35377/saucis...1273536

IZ

https://izlik.org/JA88EC58FR

Cite

RIS / Bibtex

APA

Koca, M., Avcı, İ., & Al-hayani, M. A. S. (2023). Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm. Sakarya University Journal of Computer and Information Sciences, 6(2), 80-90. https://doi.org/10.35377/saucis...1273536

AMA

1.Koca M, Avcı İ, Al-hayani MAS. Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm. SAUCIS. 2023;6(2):80-90. doi:10.35377/saucis.1273536

Chicago

Koca, Murat, İsa Avcı, and Mohammed Abdulkareem Shakir Al-hayani. 2023. “Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm”. Sakarya University Journal of Computer and Information Sciences 6 (2): 80-90. https://doi.org/10.35377/saucis. 1273536.

EndNote

Koca M, Avcı İ, Al-hayani MAS (August 1, 2023) Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm. Sakarya University Journal of Computer and Information Sciences 6 2 80–90.

IEEE

[1]M. Koca, İ. Avcı, and M. A. S. Al-hayani, “Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm”, SAUCIS, vol. 6, no. 2, pp. 80–90, Aug. 2023, doi: 10.35377/saucis...1273536.

ISNAD

Koca, Murat - Avcı, İsa - Al-hayani, Mohammed Abdulkareem Shakir. “Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm”. Sakarya University Journal of Computer and Information Sciences 6/2 (August 1, 2023): 80-90. https://doi.org/10.35377/saucis. 1273536.

JAMA

1.Koca M, Avcı İ, Al-hayani MAS. Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm. SAUCIS. 2023;6:80–90.

MLA

Koca, Murat, et al. “Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 2, Aug. 2023, pp. 80-90, doi:10.35377/saucis. 1273536.

Vancouver

1.Murat Koca, İsa Avcı, Mohammed Abdulkareem Shakir Al-hayani. Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm. SAUCIS. 2023 Aug. 1;6(2):80-9. doi:10.35377/saucis. 1273536

Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm

Öz

Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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