Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Abstract

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

Cognitive routing in software defined networks using learning models with latency and throughput constraints

“…The environment's reinforcement signal is an appraisal of the outcome, good or poor, rather than instruction on how to generate the desired behaviour. According to our findings, RL is typically employed to promote robustness and scalability [18], [19], and it enables choosing routes or route optimisation in SDNs [20], [21]. When delay minimise and throughput enhancement are used as the operation and maintenance approach for deterministic policy gradient (DDPG) routing optimization mechanism (DROM) [22], the network's performance is enhanced with reliable and superior routing services, and convergence and effectiveness are boosted.…”

Introductory Chapter: Anomaly Detection – Recent Advances, AI and ML Perspectives and Applications

“…They emphasize the use of fuzzy logic-based methods as a promising avenue for future research. Zehra et al [6] discuss the security challenges in Network Function Virtualization (NFV), advocating for machine learning-based anomaly detection techniques to enhance network security.…”

Combating the Challenges of False Positives in AI-Driven Anomaly Detection Systems and Enhancing Data Security in the Cloud

“…Reinforcement learning, where models learn by interacting with their environment and receiving feedback, is another promising approach [73]. This technique trains models to adapt their behavior based on action outcomes, useful in dynamic environments where system behavior and threats evolve.…”