Data Mining Techniques for Cloud Privacy Preservation
Keywords:
security, cloud computing, data mining, phishingAbstract
A high standard of phishing prevention became essential when it came to Internet phishing. As a result of sophisticated phishing attacks, a new mitigation challenge was created. Internet phishing has recently raised serious security and financial issues for people and businesses around. There has been a significant financial loss associated with internet services that provide a variety of communication channels, including electronic commerce, online banking, research, and online trading, as well as those that exploit both software and human weaknesses. The aim of the study is to identify the extent of works and their limitations for cloud security, to design and develop the solution for cloud security, and to evaluate the model.
We propose this new model mining model, which consists of two main bases: preprocessing and classification. the accuracy obtained in this study for malware analysis is quite high, with the highest accuracy being 95.2%.
The results of the Intrusion features, Features of Models, Malware Detection Graphs for the data, Attacks Identified from different countries for the data, Machine learning Based Model Implementation, and Accuracy of analysis were analyzed and data were obtained. Deploying sensors or agents on cloud-based systems and apps is often the first step in the data collection process for malware investigation in the cloud. Network-based intrusion detection systems (NIDS) or intrusion prevention systems are a popular way to collect data. An important paradigm that delivers the results for the grouping of requests is the application of SVM to the problem of assault detection.
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