Network Intrusion Detection Using Linear Regression

Abstract

Network security is a critical concern in today's digital age, as the proliferation of cyber threats continues to rise. Network Intrusion Detection Systems (NIDS) are essential tools for identifying and mitigating unauthorized access and attacks on network infrastructures. This paper investigates the use of linear regression for network intrusion detection, leveraging its simplicity and interpretability. We present a comprehensive mathematical derivation of the linear regression algorithm, illustrating how it can be adapted for binary classification tasks, such as distinguishing between normal and intrusive network traffic.