Implementing a NIDS System for protecting computer and wireless networks using various machine learning approaches

Abstract

In modern Wireless Networks, security is critical. With the ever-evolving attacks on wireless networks, both public and private, the use of Network Intrusion Detection Systems is at an all-time high. While NIDS is needed more than ever, its current security structure is starting to show signs of becoming obsolete. With the alarming rate of attacks in the modern digital space, NIDS needs to have a way to react effectively. Setting up NIDS is too slow and can cause many issues when defending against these attacks, leaving wireless networks vulnerable. Three approaches are often discussed NIDS: signature-based, anomaly-based, as well as hybrid. Implementing Machine Learning would fall under an updated version of Anomaly Learning. The hope from these actions is that they will allow new attacks to be caught without user interference. This paper will discuss various forms of Machine Learning, NIDS, and the implementation of both into each other. This paper will discuss our current options in Machine Learning NIDS and explain how they’ve evolved thus far and the advantages at each stage. This paper, while mainly focusing on the machine learning implementation of NIDS, will touch briefly on how this implementation could strengthen current security in wireless networks.