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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JUTI: Jurnal Ilmiah Teknologi Informasi CommIT (Communication & Information Technology) Bulletin of Electrical Engineering and Informatics Jurnal sistem informasi, Teknologi informasi dan komputer Prosiding Semnastek Bulletin of Electrical Engineering and Informatics Jurnal ULTIMA InfoSys Petir Kilat JIEMS (Journal of Industrial Engineering and Management Systems) CCIT (Creative Communication and Innovative Technology) Journal Jurnal Sistem informasi dan informatika (SIMIKA) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sisfotek Global Asean Journal of Science and Engineering (AJSE) Jurnal Sisfotek Global Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) JGGAG (Journal of Games, Game Art, and Gamification)
Warnars, Harco Leslie Hendric Spits
Doctor Of Computer Science, Bina Nusantara University
Agriculture, Biological Sciences & Forestry Arts Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Using Graph Neural Networks and CatBoost for Internet Security Prediction with SMOTE Sunge, Aswan Supriyadi; Hendric, Spits Warnars Harco Leslie; Pramudito, Dendy K.
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30157

Abstract

Internet security is the most important issue in cyberspace, on the other hand, cybercrime occurs, and the most serious threat is the theft of personal data and its misuse for the benefit of others. Although cyberspace is while internet security cannot eliminate all risks, predictive models can significantly reduce cybercrime by identifying vulnerabilities if you know how to prevent it. One of the most important things is that many internet users do not know what measures are used to avoid and whether it is safe to visit or explore, on the other hand, in system development existing studies on internet security prediction often rely on generic models that lack precision in identifying influential features or ensuring class balance in developing internet security. In this case, Deep Learning (DL) helps learn patterns from recorded data, find relevant patterns, and use the model effectively. The purpose of this study is to identify the most influential features in internet security and evaluate the effectiveness of advanced machine learning models, such as Graph Neural Networks (GNNs) and Categorical Boosting (CatBoost), for predicting internet safety. So far other studies have tested the entire data set and used a model that is generally. This is expected to lead to the design or development of systems and programs that are useful for internet security. The study used a dataset of 11,055 records with 30 features and binary classification labels ('Safe' and 'Not Safe'). To address the class imbalance, SMOTE was applied before splitting the data into training and testing sets. In testing the Graph Neural Networks (GNNs) model achieved 93.58% accuracy, 93.63% precision, 93.58% recall, and 93.55% F1-score, demonstrating its effectiveness for internet security prediction. From the results of testing the CatBoost model was used to identify key features, revealing that the 'URL of Anchor,' 'SSLFinal State,' and 'Web Traffic' have the most significant impact. From the experiments conducted, the CatBoost effectively identified features with the highest on prediction accuracy, and the GNNs model is very accurate and precise for developing applications or systems to predict internet security.
Co-Authors Achmad Imam Kistijantoro Adrian Randy Pratama Adrian Setyawan Afrilia Astari Agung A. Pramudji Agung Trisetyarso Alam, Sirojul Andhika Prasetyo Andy Julianto Antoine Doucet Arif Fahrudin Arif Fahrudin Assiroj, Priati Aswan Supriyadi Sunge Benaya Oktavianus Oktavianus Benfano Soewito Benfano Soewito Bobi Kurniawan, Bobi Calvin Leonardo Christopher Samuel Christy, Jessica Damayanti, Damayanti Darren Kent Jeremy, Darren Kent Dedy Prasetya Kristiadi Dodick Zulaimi Sudirman Edi Abdurachman Edi Abdurachman Endang Kusnadi Endang Kusnadi Epafras Suria, Epafras Erick Erick Erick Fernando Ersa Andhini Mardika Evan Fabian Rahardja Fauzi Megantara Ferry Sudarto Ford Lumban Gaol Ford Lumban Gaol Ford Lumban Gaol Ford Lumban Gaol, Ford Lumban Gilbert Xervaxius Naphan Hesananda, Rizki Hintarsyah, Aristo Putramasi Ida Farida Ignasius Raffael Santoso Joshua Dylan Jovano Edmund Friry Junaidi Junaidi Kevin Dynata Kevin Renalda Kharis Munawar Kiyota Hashimoto Kusuma Atmaja, Wahyu Haris Lukman Adyana Megantara, Fauzi Melvin Ismanto Meyliana Meyliana Meyliana, M. Muhammad Farrel Pramono Muhammad Naufal Mu’azzi Naufal Rayfi Hafizh Nizirwan Anwar Nuruliyani, Nuruliyani Po Abas Sunarya Pramudito, Dendy K. Priati Assiroj Rakhmat Arianto Randy Dwi Raymond Sunardi Oetama Richard Randriatoamanana Safrizal Safrizal Salicca Dewi Rahajeng Sandi Martono Sianipar, Nesti Fronika Siti Julianingsih Nurfitriyani suaidah suaidah Sudaryono Sudaryono Sugiarto, Dian Suharjo, Bambang Suroto Adi Tanty Oktavia, Tanty Wibowo, Adi Winarno Winarno Wiranto Herry Utomo Worapan Kusakunniran Yaya Heryadi Yaya Heryadi Yulia Lanita Zaki Izzani Akbar