期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：4
DOI：10.14569/IJACSA.2022.0130499
语种：English
出版社：Science and Information Society (SAI)
摘要：Intrusion Detection Systems(IDS) are vital for com-puter networks as they protect against attacks that lead to privacy breaches and data leaks. Over the years, researchers have formulated IDS using machine learning (ML) and/or deep learning(DL) to detect network anomalies and identify attacks. Network Intrusion Detection Systems (NIDS) within corporate networks is a form of security that detects and generates an alarm for any cyberattacks. In both academia and industry, the concept of deploying a NIDS has been studied and adopted. The majority of NIDS research, on the other hand, has focused on detecting threats that emerge from outside of a wired connection. In addition, the NIDSs recognize Wi-Fi and wired networks alike. The Wi-Fi network’s accessible connectivity distinguishes this from the wired network. A wired connection is highly resistant to many insider threats that could occur on a Wi-Fi router. A conventional view to developing NIDSs may miss malicious activities. This paper aims to design a multi-level NIDS for Wi-Fi predominant networks to identify both organizational Wi-Fi networks malicious activity and standard network malicious activity. Wi-Fi devices are common on campuses and businesses, and they are incorporated into the fixed wired network at the gateway. Wi-Fi networks are the primary target for this implementation; however, they are also designed to function in wired environments. For the Multi-Level NIDS, the proposed model used an ensemble learning method that pools the strengths of multiple weak learners into a single strong learner.
