Optimal attack detection using an enhanced machine learning algorithm
Reddy Saisindhutheja, Gopal Krishna Shyam, Shanthi Makka · International Journal of Grid and Utility Computing · 2025
As computer network and internet technologies advance more quickly today, the importance of network security is widely acknowledged. This research intends to introduce a new security platform for SaaS framework, which comprises two major phases: (1) Optimal Feature Selection and (2) Classification. Initially, the optimal features are selected from the data set. A novel algorithm named Accelerator updated Rider Optimisation Algorithm (AR-ROA), a modified form of ROA and Deep Belief Network (DBN) based Attack Detection System is proposed. The optimal features that are selected form AR-ROA are subjected to DBN classification process, in which the presence of attacks is determined. The proposed model outperforms other traditional models in aspects of Accuracy (95.3%), Specificity (98%), Sensitivity (86%), Precision (92%), Negative predictive value (97%), F1-score (86%), False positive ratio (2%), False negative ratio (10%), False detection ratio (10%), and Matthew's correlation coefficient (0.82%).