Neuroguard:Unveiling the Strength of Lightfooted Anomaly Detection with Swift-Net Neural Networks in Countering Network Threats

Abstract

In the dominion of cybersecurity, the prime tasks revolve around recognizing and moderating network breaches. This research paper impacts the widely recognized CICIDS2017 dataset to conduct a complete evaluation and comparison of numerous deep learning and machine learning representations designed for Anomaly-detection by the analysis of a diverse array of algorithms, spanning from traditional methodologies like logistic regression to more modern advances such as K-Nearest Neighbors (KNN) and state-of-the-art Swift-Net neural networks. The research also delves into the realism of employing dimensionality reduction and feature selection procedures, remarkably Principal Component Analysis (PCA) in addition with Gaussian Mixture Models (GMM). The implications of this consideration are substantial for the enhancement of network security with an emphasis of the efficiency of PCA and GMM in facilitating data visualization, enabling a deeper understanding of network behavior. Moreover, the paper highlights the potential of Swift-Net for real-time threat detection, signifying its relevance in the evolving cybersecurity environment. As the cybersecurity domain undergoes constant transformation, this research serves as a valuable reserve, paving the way for more effective Anomaly detection techniques and the employment of efficient network security solutions. These outcomes offer acute insights to reinforce network safety.