Characterizing Adversarial Behaviors in Distributed Network Architectures: A Comprehensive Classification and Detection Framework

Abstract

Distributed network systems, encompassing cloud computing, Software-Defined Networking (SDN), Internet of Things (IoT), and emerging blockchain technologies, form the backbone of modern digital infrastructure. However, their inherent complexity, dynamic nature, and expansive attack surface present significant security challenges. This article provides a comprehensive analysis of prevalent adversarial behaviors targeting these distributed environments. It proposes a classification framework for various attack patterns, detailing their mechanisms and impacts across different layers and technologies. Furthermore, it explores a range of detection approaches, from traditional signature-based methods to advanced machine learning and AI-driven techniques, evaluating their applicability and limitations in identifying sophisticated threats. The aim is to offer a holistic understanding of the threat landscape and lay the groundwork for developing more robust and adaptive security solutions for distributed network systems.