HYBRID DEEP LEARNING APPROACH TO IDENTIFY INTRUSION DETECTION WITH IMBALANCE DATASETS

Abstract

The intrusion detection system is a computer-based system that constantly identifies all types of malicious activities by monitoring the network traffic. These intrusions and doubtful activities disturb all business activities performed over the public network, such as the Internet and all connected networks. It is an essential system to provide consistent and reliable transfer of information to complete e-commerce and e-business transactions and private communication using social sites. Various deep learning techniques are used to identify security attacks by observing the typical system usage profile and to restrict all of the network traffic if it is outside the scope of the standard profile. Our proposed system is used to combine various deep learning techniques to develop a hybrid deep learning model to identify any security attack in the network. The proposed hybrid deep learning model is trained using an integrated and balanced dataset by merging already available imbalanced benchmark datasets such as NSL-KDD, ISCX, CICIDS2017, and UNSWNB15. Our proposed system is limited to identifying security attacks in benchmark datasets and restricted to available deep-learning techniques and algorithms.