Network Malware Detection for Cloud Infrastructure

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Dr. Anjaiah Adepu, Yedugani Akanksha, Dasari Gokul Pavan, Lakkarsu Aneesh Varma, Kethavath Devendher


The Many businesses are utilizing cloud resources and modern technology to run a variety of applications. These services help businesses avoid worrying about the underlying infrastructure's scalability, maintainability, and equipment monitoring. Infrastructure as a Solution (IaaS) is used by trick cloud providers (CSPs) like, Microsoft, and Google to meet the growing demand of these businesses. The safety of cloud solutions has become a top issue for CSPs due to the rising application of cloud platforms, which has made it an alluring target for the adversaries. Malware has been regarded as one of the most dangerous and damaging risks to cloud infrastructure in this regard. Any form of questionable link, file, or connection that is created or received through the network is known as malware or network malware. Malware is an incident that poses a risk to an organization's security and has the potential to compromise your computer. This research uses a data set made up of Network Integrity Features, Network Performance Traits, and Network Constituents Features with Gaussian process classification and Decision Tree Classification to forecast network malware. There are 4 modules in the assignment. Piling Analysis and Multi-Layer Perceptron Analysis are the two new classifiers that are added to the GUI of the main module. The following component controls how Multi-Layer Perceptron Evaluation is carried out. The following component controls how Multi-Layer Perceptron Evaluation is carried out. The Stacking Evaluation is handled by the third component. The fourth component compares the four classifiers' accuracy rates according on the exam's age.

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