SELEKSI FITUR MENGGUNAKAN MUTUAL INFORMATION UNTUK DETEKSI INTRUSI
DOI:
https://doi.org/10.54314/jssr.v8i3.3112Abstrak
Abstract: This study explores the use of Mutual Information (MI) for feature selection in intrusion detection, focusing on the CICIDS 2017 dataset. Given the complexity and large volume of data in intrusion detection systems, this research aims to identify the most informative features. The methodology includes data preprocessing, MI calculation, and feature selection based on the highest MI values. The analysis results indicate that using MI contributes to improving model accuracy and reducing the false positive rate. These findings underscore the importance of feature selection in enhancing the effectiveness of intrusion detection systems and provide significant contributions to developing more efficient cybersecurity strategies.
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Keyword: IDS, Mutual Information, CICIDS2017, Feature selection
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Abstrak: Penelitian ini mengeksplorasi penggunaan Mutual Information (MI) untuk seleksi fitur dalam deteksi intrusi, dengan fokus pada dataset CICIDS 2017. Mengingat kompleksitas dan volume data yang besar dalam sistem deteksi intrusi, penelitian ini bertujuan untuk mengidentifikasi fitur-fitur yang paling informatif. Metodologi yang diterapkan mencakup preprocessing data, perhitungan MI, dan seleksi fitur berdasarkan nilai MI tertinggi. Hasil analisis menunjukkan bahwa penggunaan MI berkontribusi pada peningkatan akurasi model serta pengurangan tingkat false positive. Temuan ini menegaskan pentingnya seleksi fitur dalam meningkatkan efektivitas sistem deteksi intrusi dan memberikan kontribusi signifikan dalam pengembangan strategi keamanan siber yang lebih efisien.
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Kata kunci: IDS, Mutual Information, CICIDS2017, Seleksi fitur
Unduhan
Referensi
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