Abstract: 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.

Abhijit Das and Pramod, “Design and Development of an Efficient Network Intrusion Detection System using Ensemble Machine Learning Techniques for Wifi Environments” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130499

@article{Das2022,
title = {Design and Development of an Efficient Network Intrusion Detection System using Ensemble Machine Learning Techniques for Wifi Environments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130499},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130499},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Abhijit Das and Pramod}
}