Title: Optimal attack detection using an enhanced machine learning algorithm

Authors: Reddy Sai Sindhu Theja; Gopal K. Shyam; Shanthi Makka

Addresses: Department of CSE, Vardhaman College of Engineering, Hyderabad, Telangana, India ' School of CSE, Cloud Computing Lab, Presidency University, Bengaluru, Karnataka, India ' Department of CSE, Vardhaman College of Engineering, Hyderabad, Telangana, India

Abstract: 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%).

Keywords: software-as-a-service; SaaS framework; security; ROA; optimisation; DBN; attack detection system; feature selection; classification; machine learning algorithms; security issues.

DOI: 10.1504/IJGUC.2025.143875

International Journal of Grid and Utility Computing, 2025 Vol.16 No.1, pp.1 - 14

Received: 05 Aug 2022
Received in revised form: 19 Nov 2022
Accepted: 25 Nov 2022
Published online: 12 Jan 2025 *