I am doing a research project for my Bachelor of IT (honours) on Machine Learning for Cloud Security.
This research paper discusses the Fraudulent Resource Consumption (FRC) Attack and uses Support Vector Machines (SVM) to detect cloud-based FRC attacks. Fraudulent Resource Consumption (FRC) attacks are created by slowly using cloud services' metered resources. The attacker's goal is to abuse the utility pricing model by stealing cloud resources. This skilful resource overuse results in a significant cost burden for the client. These assaults employ low-intensity HTTP requests per hour, like legitimate users. Due to this, FRC attacks are difficult to detect. FRC is an Economic Denial of Service (EDoS) attack that targets cloud adopters' financial resources by increasing their costs. Unlike DDoS assaults, which can temporarily block legitimate users from accessing services, EDoS attacks can significantly increase cloud users' costs. Support-vector machines (SVMs, also known as support-vector networks) are supervised learning models that examine data for classification and regression analysis.
Now I want guidance for a script that Can capture this generated FRC traffic, run SVM on it for training and then run SVM on it for testing. Training can be one script, and testing can be another
I shall be highly grateful if you could kindly guide me in this.
Thanks & regards,
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