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All Journal The Indonesian Journal of Computer Science
Natasha Clarissa Maharani
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Klasifikasi Serangan Jaringan menggunakan Teknik Imputasi Berbasis Jaringan Syaraf Tiruan Safrizal Ardana Ardiyansa; Eric Julianto; Natasha Clarissa Maharani; Haidar Ahmad Fajri
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4349

Abstract

Rapid technological developments have changed access to information significantly, especially in telecommunications. This growth creates new threats, such as network attacks, so detection becomes critical for network security. Leveraging machine learning algorithms to detect threats is promising, with effectiveness largely dependent on selecting relevant features optimized by the bat algorithm. Data imputation is critical in preparing data sets, and neural network-based imputation techniques demonstrate outstanding performance, achieving accuracy rates of 99.4% on validation data and 99.3% on test data. This method consistently maintains precision, recall, and scores around 98%. Models using this method also approach perfection in classifying normal and neptune labels. This imputation method can also be applied to other model architectures using autoML. Alternative models such as Light GBM, XGBoost, Random Forest, Extra Trees, and Weighted Ensemble L2 also exhibit exceptional accuracy, exceeding 99.8%.
Co-Authors Eric Julianto Haidar Ahmad Fajri Safrizal Ardana Ardiyansa