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All Journal Format : Jurnal Imiah Teknik Informatika TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Monas: Jurnal Inovasi Aparatur Jurnal Komputer dan Elektro Sains
Andrie Yuswanto
Institut Teknologi Budi Utomo
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Social Sciences Other

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IMPLEMENTASI METODE ADASYN DALAM DETEKSI URL BERBAHAYA MENGGUNAKAN MACHINE LEARNING: DEMI MENINGKATKAN KEAMANAN SIBER DI ERA DIGITAL Gilang Dwi Setyawan; Andrie Yuswanto; Ahmad Maulid Ridwan; Budi Wibowo; Maman Firmansyah
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.153

Abstract

Cybercriminals exploit malicious URLs as a distribution channel to spread harmful software across the internet. They take advantage of vulnerabilities in browsers to install malicious software with the aim of gaining remote access to the victims' computers. Typically, this malicious software aims to gain access to networks, steal sensitive information, and silently monitor targeted computer systems. In this research, a data mining approach known as Classification Based on Association (CBA) is employed to detect malicious URLs using both the URL itself and the features of the presented web pages. The CBA algorithm utilizes a training dataset of URLs as historical data to discover association rules that can be used to create an accurate classifier. By detecting dangerous URLs and malicious software, this contribution can assist organizations and individual users in enhancing the security of their computer systems and networks, thereby protecting sensitive data and reducing the risk of security incidents. The experimental results demonstrate that CBA achieves performance on par with tested classification algorithms, achieving an accuracy of 99% and low rates of false positives and false negatives. Future research could expand its focus to detect malicious URLs and software on mobile devices and embedded systems, as they have become significant targets for cybercriminals.
Co-Authors Abdur Rohman Ardi Agung Ahmad Maulid Ridwan Aji Susanto Andi Tri Setyohadi Budi wibowo Deny Danu Prasetia Fyan Isra Wardanu Gilang Dwi Setyawan Lela Mariya Maman Firmansyah Mohammad Ibrahim Qhansa Aditia Santi Rahmawati Sarah Azzahra Suka Hermawan Toto Dwi Sugiarto