Flexible and Robust Real-Time Intrusion Detection Systems to Network Dynamics
Kicho Yu, Khanh Nguyen, Younghee Park
Abstract
Deep learning-based intrusion detection systems have advanced due to their technological innovations such as high accuracy, automation, and scalability, to develop an effective network intrusion detection system (NIDS). However, most of the previous research has focused on model generation through intensive analysis of feature engineering instead of considering real environments. They have limitations to applying the previous methods for a real network environment to detect real-time network attacks. In this paper, we propose a new flexible and robust NIDS based on Recurrent Neural Network (RNN) with a multi-classifier to general a detection model in real-time. The proposed system adaptively and intelligently adjusts the generated model with given system parameters that can be used as security parameters to defend against the attacker’s obfuscation techniques in real-time. In the experimental results, the proposed system demonstrated that the proposed system detects network attacks with a high accuracy and high-speed model upgrade in real-time while showing robustness under an attack.