Cloud Malicious Threat Detection Using Convolution Filter and EBPNN
Rashmi Singh, Praveen Kumar Mannepalli · 2021 5th International Conference on Information Systems and Computer Networks (ISCON) · 2021
As number of online services are conducted on cloud due to its flexibility and operational cost, so some of the malicious activity are performed which needs to be detected under cloud security. This paper has traced some patterns from the cloud environment which help to identify traffic into normal or malicious class. This learning is done by passing the training dataset through convolution filter first than filtered traffic were further used for the training of the Neural Network having back propagation learning technique was learning. This learning gives a two class output (Normal, Malicious). UNSW-NB15 dataset was used for the implementation of training and testing of this work. Experimental values of proposed model was compared with existing techniques and it was obtained that proposed model increased the detection accuracy of the work.