Analysing Network Traffic and Implementing Diverse Technologies to Examine Different Components of the Network

This paper comprehensively explores network traffic analysis using various technologies, focusing on dynamic analysis for malware study with Sandnet as an analysis environment. It investigates DNS and HTTP usage by malware, considers ethics and risk mitigation, and delves into machine learning-based IoT device identification. The study introduces a multi-stage meta classifier methodology to enhance IoT device classification, discusses network traffic predictability, and highlights intrusion detection using packet sniffers for security. By addressing these topics, the paper significantly contributes to network traffic analysis understanding, its application, and implications for network security and control.