Real-time Anomaly Detection in SDN Architecture using Integrated SIEM and Machine Learning for Enhancing Network Security

The Software-Defined Networking (SDN) paradigm has introduced heightened flexibility and scalability to network infrastructure management. However, the centralized control plane inherent in SDN architectures is susceptible to an array of security vulnerabilities, necessitating the development of efficient and real-time anomaly detection systems. This paper presents a novel integrated methodology for real-time anomaly detection within SDN architectures, capitalizing on the synergies between Security Information and Event Management (SIEM) systems and advanced machine learning techniques to bolster network security. The proposed framework operates by seamlessly collecting and analyzing live network traffic data, promptly pinpointing potential anomalies, and subsequently correlating these events via the SIEM system. To enhance accuracy while mitigating false positives, machine learning algorithms are harnessed to accurately categorize network traffic into benign and malicious activities, dynamically adapting to evolving threat landscapes. Empirical validation is conducted through an exhaustive dataset of real-world network traffic, encompassing an extensive array of attack scenarios. Findings vividly underscore the efficacy of the amalgamated SIEM and machine learning-driven anomaly detection system, yielding impressive detection accuracy while maintaining notably low rates of false positives. Noteworthy is the system's intrinsic adaptability to emergent threats, culminating in an elevated caliber of network security and fortitude within the SDN domain. This contribution significantly enriches the realm of real-time anomaly detection research, endowing SDN architectures with a pioneering strategy to counteract intricate cyber threats effectively.