Enhancement of Attacks Prediction on Cloud Computing using ResNet 50 Algorithm

The goal performance in predicting attacks using Resnet 50 algorithm and compared with Logistics Regression algorithm is proposed in this paper. The maximum accuracy performance analysis in Resnet $50(\mathrm{~N}=10)$ is compared with logistic regression, which identifies and measures accuracy and distance. Identification is done by using a Data set to enhance the performance and classification of 10 and G Power of 0.8 with CI ${9 5} \%$ The attack detection classification dataset was taken from Kaggle in total size of ${2 0 2 0 0}$. Accuracy is used to evaluate the performance of Resnet 50. Novel ResNet 50 ($98.98 \%$) identifies objects and improves measurement accuracy over logistic regression $82.98 \%$. The research results are supported by the independent sample T-test value, and a p-value of 0.001 (Independent T-test sample $\mathrm{p}\lt 0.05$) shows that there is a statistically significant difference between the two groups under study. Novel ResNet50 has more enhanced accuracy than logistics regression in predicting attacks on cloud platform.