ENERGY EFFICIENT INTRUSION DETECTION FOR NFV CLOUD INFRASTRUCTURES USING MACHINE LEARNING TECHNOLOGY
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Abstract
Recent times have seen a steady shift of technology from traditional software models to the cloud.
Widespread deployments of Network Function Virtualization (NFV) technology will replace many physical appliances in telecommunication networks with software executed on cloud platforms. Setting compute servers continuously to high-performance operating modes is a common NFV approach for achieving predictable operations. Intrusion detection systems are one of the most suitable security solutions for protecting cloud based environments. The Dynamic Voltage-Frequency Scaling (DVFS) technology available in Intel processors is a known option for adapting the power consumption to the workload, but it is not optimized for network traffic processing workloads. This paper proposes energy efficient intrusion detection for NFV Cloud infrastructures using Machine learning technology. Attack presence is confirmed by performing Machine learning technique with belief propagation. Performance measures such as Detection Rate and Accuracy are used to evaluate the performance of the approach. From results obtained accuracy is 94.11% and achieved detection rate as 70 sec. transmission rate of proposed method is better than compared to setting the maximum frequency or using the ondemand governor.
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