CatBoost untuk Deteksi Worm dengan Dataset UNSW-NB15 menggunakan MDI-VAE Untuk Memecahkan Masalah Label yang Tidak Seimbang
Abstrak
Abstrak Apa. Label serangan worm ada pada dataset UNSW-NB15, tetapi kondisi ketidakseimbangannya parah. Tujuan penelitian ini adalah untuk menganalisis efektivitas variational autoencoder (VAE) dalam mengatasi kondisi ketidakseimbangan di dataset UNSW-NB15. Mengapa. Studi ini menggunakan salah satu kelas minoritas dalam dataset UNSW-NB15, yaitu kelas worm. Pada tahap selanjutnya, studi ini melakukan seleksi fitur menggunakan mean decrease in impurity (MDI) dengan pengklasifikasi ExtraTrees (ETC) sebagai dasar dan membandingkannya dengan metode terkini, random forest (RF). Bagaimana. Kami menggunakan akurasi, presisi, recall, dan F1-score untuk membandingkan kedua metode seleksi fitur tersebut. Kemudian, kami mengusulkan model ensemble berdasarkan category boosting (CatBoost) dan membandingkan performanya dengan adaptive boosting (AdaBoost) menggunakan sensitivitas, spesifisitas, dan geometric mean (G-mean). Pada tahap akhir, kami mengevaluasi efek VAE dalam menghasilkan data sintetis untuk menyeimbangkan data pelatihan. Hasil utama. Hasil pengujian menunjukkan bahwa CatBoost yang menggunakan MDI ETC mengungguli MDI RF, mencapai skor presisi dan G-mean masing-masing sebesar 0,80 dan 0,86, dibandingkan dengan 0,61 dan 0,68. CatBoost mengungguli AdaBoost dalam hal sensitivitas dan G-mean, mencapai 0,97 dan 0,99, dibandingkan dengan 0,67 dan 0,82 untuk AdaBoost. Terakhir, penerapan VAE tidak memiliki efek signifikan pada AUC CatBoost, sedangkan MDI+VAE meningkatkan skor tersebut dari 0,987 menjadi 0,995. Kata kunci: : sistem deteksi intrusi, ketidakseimbangan kelas, variational autoencoder, UNSW NB15, peningkatan boosting.
Unduhan
Referensi
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