FT KLASIFIKASI SERANGAN DISTRIBUTED DENIAL OF SERVICE MENGGUNAKAN ENSEMBLE STACKING
Abstract
Serangan Distributed Denial of Service (DDoS) merupakan jenis serangan siber yang bertujuan untuk membuat layanan atau sumber daya jaringan tidak dapat diakses oleh pengguna yang sah dengan membanjiri lalu lintas jaringan secara masif. Pola serangan DDoS yang semakin kompleks dan bervariasi menuntut adanya sistem deteksi yang tidak hanya andal, tetapi juga adaptif terhadap berbagai jenis serangan. Sebagian besar penelitian sebelumnya masih terbatas pada klasifikasi biner sehingga kurang efektif dalam menghadapi tantangan klasifikasi serangan yang lebih beragam. Penelitian ini bertujuan untuk mengembangkan model Intrusion Detection System (IDS) berbasis machine learning dengan pendekatan ensemble learning untuk klasifikasi multiclass serangan DDoS. Model ini dibangun menggunakan pendekatan stacking, dengan K-Nearest Neighbors, Decision Tree, Naive Bayes, dan Support Vector Machine sebagai base learners, serta Logistic Regression sebagai meta learner. Dataset CIC-DDoS2019 digunakan sebagai sumber data untuk proses pelatihan dan pengujian model. Hasil evaluasi menunjukkan bahwa model ensemble stacking memberikan kinerja terbaik dengan accuracy sebesar 78,8%, F1-score sebesar 78,4%, dan nilai AUC tertinggi sebesar 0,982. Dengan demikian, pendekatan ensemble learning terbukti mampu meningkatkan kinerja dan keakuratan sistem deteksi serangan DDoS dalam skenario klasifikasi multiclass dibandingkan model individual.
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