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All Journal CESS (Journal of Computer Engineering, System and Science) Engineering, Mathematics and Computer Science Journal (EMACS) Journal of Student Research Exploration
Pastika, Puan Bening
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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Machine Learning-Based Malicious Website Detection Using Logistic Regression Algorithm Pastika, Puan Bening; Alamsyah, Alamsyah
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11844

Abstract

Cybercrime is an increasing threat that occurs while exploring the internet. Cybercrime is committed by cybercriminals who exploit the web's vulnerability by inserting malicious software to access systems that belong to web service users. It is detrimental to users, therefore detecting malicious websites is necessary to minimize cybercrime. This research aims to improve the effectiveness of detecting malicious websites by applying the Logistic Regression algorithm. The selection of Logistic Regression is based on its ability to perform binary classification, which is important for distinguishing between benign and potentially malicious websites. This research emphasizes a preprocessing stage that has been deeply optimized. Data cleaning, dataset balancing, and feature mapping are enhanced to improve detection accuracy. Hybrid sampling addresses data imbalance, ensuring the model is trained with representative data from both classes. Experimental results show that the Logistic Regression implementation achieves an excellent level of accuracy. The developed model recorded an accuracy of 92.60% without cross-validation, which increased to 92.71% with 5-fold cross-validation. The novelty of this research lies in the significant increase in accuracy compared to previous methods, demonstrating the potential to improve protection against malicious website threats in an increasingly complex and risky digital environment. This research makes an important contribution to the development of digital security detection technologies to address the ever-growing challenges of cybercrime.
Co-Authors Alamsyah - Anggraeni, Dinda Ayu Florentina Yuni Arini, Florentina Yuni Hernawan, Yoga Ramadhan, Farhan Husyen Septiana, Dina Wachidah