Securing the Cloud Storage Using Novel Machine Learning-based Intrusion Detection System - Vishal Sharma, Sadaf Hashmi, Deepak Minhas, Deeksha Chaudhary, Gowrishankar Jayaraman (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Intrusion detection is an approach for detecting unauthorized access or malicious behavior inside a system. When applied to cloud storage, it entails monitoring and analyzingnetwork activity, system records and consumer activity to identify any possible security vulnerabilities or abnormalities. This proactive method assists in securing sensitive data and the integrity of cloud-based infrastructure, ensuring persistent security against cyber-attacks. In this study, we produced an intelligent intrusion detection algorithm named Gorilla Troops fine-tuned Modified Random Forest (GTO-MRF) for enhancing security in cloud storage. Initially, we collected a dataset comprising simulated cloud-based intrusion scenarios and a variety of attack types. The proposed model was evaluated using this diverse dataset to enhance security measures. UnitVector Transformation (UVT) algorithm is employed to pre-process the gathered raw data. We extracted primary features from the pre-processed data using Kernel Principal Component Analysis (KPCA). The Gorilla Troops Optimization (GTO) approach improves the approach by adjusting the tree architectures and feature importance weights. We implemented the proposed model in software. The result evaluation phase is performed with multiple metrics such as training time, False alarm rate and encryption time to evaluate the suggested GT-MRF approach. We conducted a comparison analysis with other conventional methodologies. The experimental results illustrate that the proposed GT-MRF approach performed better than other conventional approaches for enhanced intrusion detection models in cloud storage.