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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Teliska Jurnal technoscientia ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika PROtek : Jurnal Ilmiah Teknik Elektro ITEj (Information Technology Engineering Journals) Sistemasi: Jurnal Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining Jurnal RESISTOR (Rekayasa Sistem Komputer) Patria Artha Technological Journal Building of Informatics, Technology and Science Jurnal Teknik Industri Terintegrasi (JUTIN) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Aptekmas : Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika
Fadhli, Mohammad
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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.
Implementing Zero Trust Model for SSH Security with kerberos and OpenLDAP Mediana, Salwa Deta; lindawati, lindawati; Fadhli, Mohammad
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3330

Abstract

In order to remove trust presumptions towards the internal network, this study addresses the use of the Zero Trust Model in SSH (Secure Shell) security. The study approach is conducting tests by incorporating the Kerberos and OpenLDAP protocols into the SSH infrastructure. While OpenLDAP acts as a central directory for user management and permission access, Kerberos is utilized for single authentication and security resources like Kerberos tickets. As the server operating system for this investigation, Debian was used. Strong justification exists for securing SSH with Kerberos and OpenLDAP. SSH protocol assaults commonly target the standard port 22 (SSH), which is used for SSH. To ensure the security and integrity of the server system, the SSH port must be protected with Kerberos and OpenLDAP. SSH access is limited by Kerberos single authentication, which lowers the possibility of brute-force assaults and password theft. User administration and authorisation are facilitated by the integration of OpenLDAP. Implementing the Zero Trust strategy enables strong authentication and defends the system from insider threats. The system is protected from internal and external network assaults thanks to robust authentication, accurate authorisation, and isolating internal and external networks. An essential step in maintaining the security of the server system, data integrity, and information confidentiality is to secure port 22 and improve SSH with this integration. The research findings show that applying the Zero Trust model through this protocol integration greatly improves system security, resulting in better authentication and authorisation.
Perbandingan Algoritma Support Vector Machine dan Bi-Directional Long Short Term Memory Dalam Mengklasifikasi Berita Hoaks Merinda, Siska; Ciksadan, Ciksadan; Fadhli, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7391

Abstract

The rapid advancement of digital technology has made it easier to spread information widely and quickly. However, this ease of access has also contributed to the rise of false or misleading news, commonly known as hoaxes, which can confuse the public. This study aims to evaluate and compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Bi-Directional Long Short Term Memory (BiLSTM), in classifying hoax news written in Indonesian. The research adopts a supervised learning approach, where models are trained using pre-labeled data categorized as either hoax or non-hoax. The process begins with collecting data from trusted sources, followed by several preprocessing steps, including text cleaning, tokenization, stopword removal, and stemming. After preprocessing, the dataset is split into training and testing sets in an 80:20 ratio. The results show that the SVM model achieved an accuracy of 98.46%, with 98% precision and 99% recall for the non-hoax category. In comparison, the BiLSTM model performed better, reaching 99% accuracy, with both precision and recall at 99% for both categories. These findings indicate that BiLSTM is more effective at capturing linguistic context and identifying patterns in hoax-related content. Additionally, the models were implemented into a web-based system to assess their real-world detection capabilities.
Co-Authors Ade Rosta Paramitha, Nadila Affrylia, Gita Aliffiyah, Muhammad Bayu Arsella, Shendy Cahya Iman, Tri Rizki Ciksadan, Ciksadan Deta Mediana, Salwa Irma Salamah LINDAWATI Lindawati Lindawati Maharani, Fistania Ade Putri Martinus Mujur Rose Mediana, Salwa Deta Merinda, Siska Mujur Rose, Martinus Raihan, Ahmad Respati, Rayhan Dhafir Saifuddin Z.S, Joumil Aidil Saifuddin, Joumil Aidil Salsabila, Meidita Savitri, Yulivia Rhadita Septiani, Dinda Sopian Soim, Sopian Suni, Gina Amanah Suroso SURYANI, TRIA ANANDA Tarnita Rizky Prihandhita Ulima Achva, RA. Jihan Utomo, Tri Binarko Winursito, Yekti Condro Wulandari, Fadhilah Dwi