An effective NIDS framework based on a comprehensive survey of feature optimization and classification techniques

The technological advancement leads to an increase in the usage of the Internet with many applications and connected devices. This increased network size causes increased complexity and creating rooms for the attackers to explore and exploit vulnerabilities to carry out various attacks. As a result upsurge of network attacks can be realized in recent years and is diversified, which can be affirmed by the admittance of various organizations. Varieties of intrusion detection systems (IDSs) have been designed and proposed to tackle such issues based on the misuse-based, anomaly based, and sometimes hybrid techniques. The high rate of network data generation and its enormous volume makes it challenging for IDSs to maintain their efficacy and reliability. This paper discusses a comprehensive understanding of IDS types, six benchmark network datasets, high distributed dimensionality reduction techniques, and classification approaches based on machine learning and deep learning for intrusion detection with their importance to ascertain the efficacy and reliability of IDSs. Furthermore, based on the literature review, a general framework for NIDS has been proposed. At last model for network IDS (NIDS) is designed by following the proposed framework. Achieved accuracy and detection rate of the proposed NIDS model on the UNSW-NB15 dataset are 98.11% and 97.81%, respectively, and achieving better performance than other approaches comparatively.