Using Graph Neural Networks and CatBoost for Internet Security Prediction with SMOTE

Sunge, Aswan Supriyadi and Hendric, Spits Warnars Harco Leslie and Pramudito, Dendy K. (2024) Using Graph Neural Networks and CatBoost for Internet Security Prediction with SMOTE. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 10 (4). pp. 747-762.

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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.

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	06 Feb 2025 01:34
Last Modified:	06 Feb 2025 01:34
URI:	http://eprints.uad.ac.id/id/eprint/79756

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