Utilizing machine learning techniques for network traffic anomaly detection

Abstract

The landscape of network traffic anomaly detection has evolved considerably in recent times, driven largely by the advent of pioneering algorithms. This article undertakes an exhaustive comparative exploration of some of the most contemporary algorithms, namely Prophet, Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Isolated Forest (IF), and OmniAnomaly. Delving deep into their distinctive features, functionalities, and practical applications, the paper sheds light on the factors that render these algorithms superior to their predecessors. The discourse commences with a prologue underscoring the escalating importance of network traffic anomaly detection, especially in the face of the burgeoning cyber threats of the modern era. This sets the stage for a presentation on the focal algorithms - Prophet, RNN, CNN, IF, and OmniAnomaly. Each algorithm is then dissected to provide readers with a nuanced understanding of its underlying mechanics and methodologies. Furthermore, the discourse amplifies the breakthroughs and innovations underpinning each algorithm, highlighting attributes such as heightened accuracy, lucid interpretability, proficiency in deciphering intricate patterns, and the agility to detect anomalies in real time. Factors like computational agility, resilience, structural intricacy, and versatility across varied operational terrains are assessed in a meticulous comparative framework. Drawing from empirical evidence available in extant literature, the article underscores the stellar performance of these algorithms, benchmarked using quantitative metrics like precision.