INTEGRASI NORMALIZED RELATIVE NETWORK ENTROPY DAN NEURAL NETWORK BACKPROPAGATION (BP) UNTUK DETEKSI DAN PERAMALAN SERANGAN DDOS

Arif Wirawan Muhammad, Faza Alameka

Abstract


Distributed denial-of-service (DDoS) merupakan jenis serangan dengan volume dan intensitas DDoS terus meningkat dengan biaya mitigasi yang terus meningkat seiring berkembangnya skala organisasi. Penelitian ini memiliki tujuan untuk mengembangkan sebuah pendekatan baru untuk mendeteksi dan membentuk cluster jenis serangan DDoS, berdasarkan pada karakteristik aktivitas jaringan dengan mengintegrasikan metode Normalized Relative Network Entropy (NRNE) sebagai estimator awal terhadap anomali aktivitas jaringan, dan metode Neural Network Backpropagation (BP) sebagai fungsi supervised learning terhadap pola anomali berdasarkan output dari NRNE. Data training yang digunakan dalam adalah log file dari KDD Cup 1999 yang diterbitkan oleh DARPA. Untuk pengujian real-world attack, digunakan data yang diterbitkan oleh CAIDA 2007. Pengujian simulation attack digunakan software DDoS Generator. Pengujian normal traffic digunakan data CAIDA 2011. Adanya pendekatan baru dalam mendeteksi serangan DDoS, diharapkan bisa menjadi sebuah komplemen terhadap sistem IDS dalam meramalkan terjadinya serangan DDoS.

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