Cloud Base Intrusion Detection System using Convolutional and Supervised Machine Learning
Aditya Kumar Shukla, Ashish Kumar Sharma · 2023
The ubiquity and pay-for-use services offered by cloud computing draw more customers to use it as a result of these facilities. Even if cloud computing has many benefits, there are also some drawbacks. Cloud security features including confidentiality, availability, and integrity are susceptible to assaults. Therefore, security solutions are necessary for both cloud service providers and consumers in order to identify assaults and enhance cloud security. The biggest problem with cloud computing is security. The cloud has a variety of incursions. People are becoming increasingly conscious of the significance of network security as computer network technologies advance swiftly and internet technology develops more quickly. The greatest problem in computing is network security because assaults are happening more frequently. Due to these factors, a series of techniques known as intrusion detection systems (IDSs) has developed to prevent unauthorized use of a network’s resources. In this study, we will explore convolutional deep learning method along with supervised methods likes support vector machines and KNN models, and we will propose a hybrid solution for intrusion detection using CNN, SVM and KNN models.