Enhancement of Attacks Prediction on Cloud Computing using ResNet 50 Algorithm
V R Vijai Hariharan, Sigamani Ashokkumar, Mani Tamilselvi · 2024
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.