Virtual machines in Infrastructure as a Service environment based on a shared responsibility model involves a lot of security vulnerabilities. Virtual machines communicate with others, after a single virtual machine is attacked, the software infrastructure will be threatened by malicious programs. We propose an anomaly detection method for virtual machine malicious processes based on GRU model. This method could collect system call information in the virtual machine in a no-agent way. This detection model is based on GRU, which can detect the abnormal behavior of processes in virtual machines and experiments prove that this method could have a good performance in malicious behavior detection.
The designing of network performances indexes are critical to respond to the factors that assess network performances. In this study, a novel assessment method for designing network performance indexes based on combined trapezoidal and intuitionistic fuzzy information axiom. First, the network performances indexes are designed with the customer-expected functional requirement (FR) and the existing FR as the design range and the system range respectively. Then the information content of qualitative and quantitative indexes, which facilitates the selection of the best alternative with minimum information content, are calculated separately and standardized. To solve this issue of the integral characterised by fuzzy bounds, this study proposes a defuzzification method to convert fuzzy bounds into crisp numbers. Finally, the assessment is performed to demonstrate the effectiveness of the developed approach.
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