DETEKSI ANCAMAN URL BERBAHAYA BERBASIS EMBEDDING FEATURE EXTRACTION MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK

ZUHRI, FARHAN RADHI and Prasetyo, Aditya Putra Perdana and Abdurahman, Abdurahman (2025) DETEKSI ANCAMAN URL BERBAHAYA BERBASIS EMBEDDING FEATURE EXTRACTION MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Malicious URLs are often used in cyber attacks, especially in an attempt to steal sensitive information, spread malware or commit online fraud. Cybercriminals will usually spread these attacks through fake advertisements, email spam and various other ways to attract victims' attention. There are various ways to prevent these attacks from continuing to occur. This research proposes an innovative approach by integrating subword embedding technique for feature extraction. SMOTE is used to overcome the imbalance between classes. An artificial neural network (ANN) classification model is proposed considering its superiority in capturing more complex information.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Cybersecurity, URL Berbahaya
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Farhan Radhi Zuhri
Date Deposited:	25 Aug 2025 03:52
Last Modified:	25 Aug 2025 03:52
URI:	http://repository.unsri.ac.id/id/eprint/183172

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