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All Journal Paradigma Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Jurnal Teknik Informatika (JUTIF) Al-Ahwal Al-Syakhsiyyah: Jurnal Hukum Keluarga dan Peradilan Islam International Journal of Finance Research Journal of Maritime Empowerment Agrikan: Jurnal Agribisnis Perikanan Journal of Student Research Exploration Riset Sains dan Teknologi Kelautan JUSTIMES (Jurnal Rekayasa Teknik Mesin Saburai) Majalah Pengabdian Indonesia Journal of Sustainable Economic and Business
Raihan, Ahmad
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

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IMPLEMENTATION OF DEEP LEARNING FOR DETECTING PHISHING ATTACKS ON WEBSITES WITH COMBINATION OF CNN AND LSTM Raihan, Ahmad; Fadhli, Mohammad; Lindawati, Lindawati
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2446

Abstract

Phishing attacks represent significant cyber threats to internet users, particularly on websites. These attacks are conducted by perpetrators seeking to acquire victims' data by impersonating legitimate websites. To address this threat, a solution is proposed using deep learning with a combined algorithm of convolutional neural network and long short-term memory. The research methodology included data collection comprising phishing and legitimate website links, pre-processing through tokenization, padding, and labeling, and splitting data into training and testing sets. The models were then trained, and grid search was employed to identify the optimal hyperparameters for each algorithm. The algorithm’s performance was calculated by accuracy, precision, recall, and F1-score metrics. The outcomes indicated that using the combination algorithm achieved 95.63% accuracy, 94.60% precision, 96.81% recall, and 95.78% f1-score. This paper concludes the proposed algorithm is effective in detecting phishing attacks on websites.
Co-Authors Adi Susanto Alansar, Toufik Alfajri, Muh Ari Beni Santoso Asnidar Desi Desi, Desi Efendi, Iswar Fadhli, Mohammad Fairuz, Najla Tasya Falyajaya, Fatthah Ikhwan Fatma Hafidzi, Anwar Hendrawan Syafrie Hery Sutrawan Nurdin, Hery Sutrawan Kanza, Adam Satrio Kartomo, Kartomo Kaswar , Andi Baso Khairani, Dina Zhafira Khalifa, Muta Ali Lindawati Lindawati M. Yunus Marsista Buana Putri, Marsista Buana Muhammad Syaiful Munandar, Erik Musaddiq, M. Azhar Nela Agustin Kurnianingsih Oktavianus, Fendy Putriani, Dian Rohma Rahmadinanti, Nabila Azahara Ramadhan, Khalid Ahmad Rezky, Rezky Ririn Irnawati Rohmah, Ahlam Umniyatur S.Intam, Rezki Nurul Jariah Septayuda, Irwan Sofiani, Nita Sulistyono, Bakti Thohirin, Muh Wiguna, Anindita Nanda Wisnaningsih, Wisnaningsih Wiwin Agustian Yanto Ramli Yudha Rahman Yusran Yusran