Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Automated Power Control for Virtualized Infrastructures

Power control for virtualized enviromnents has is keeping underlying infrastructure in reasonably low power gained much attention recently. One of the major challenges states and achieving service-level objectives （SLOs） of upper applications as well. Existing solutions, however, cannot effectively tackle this problem for virtualized environments. In this paper, we propose an automated power control solution for such scenarios in hope of making some progress. The major advantage of our solution is being able to precisely control the CPU frequency levels of a physical environment and the CPU power allocations among virtual machines with respect to the SLOs of multiple applications. Based on control theory and online model estimation, our solution can adapt to the variations of application power demands. Additionally, our solution can simultaneously manage the CPU power control for all virtual machines according to their dependencies at either the application-level or the infrastructure-level. The experimental evaluation demonstrates that our solution outperforms three state-of-the-art methods in terms of achieving the application SLOs with low infrastructure power consumption.