OPIN-ITP: Optimized Physics Informed Network with Trimmed Score Regression Based Insider Threats Prediction in Cloud Computing

Abstract

INTRODUCTION: Insider threats are a major issue for cyber security. In contrast to external attackers, insiders have more privileges and authorized access to data and resources, which can cause an organization great harm. To completely understand an insider's activities throughout the organization, a more sophisticated method is needed. OBJECTIVES: Based on an organization's login activity, this study proposes a novel conceptual method for insider threat detection. Behavioural activities such as HTTP, Email and Login details are collected to create a dataset which is further processed for pre-processing using data transformation and Trimmed Score Regression (TSR). METHODS: These pre-data are given to the feature extraction process using Deep Feature Synthesis (DFS) extraction. The extracted data are fed to Physics Informed Neural Networks (PINN) for insider threat detection. RESULTS: The prediction process of PINN was improved through optimally choosing parameters such as learning rate and weight using Hunter-prey Optimization (HPO). The proposed model offers 68% detection rate, 98.4% accuracy, 5% FDR, 95% F1_score and 0.7005 sec execution time. CONCLUSION: Observed outcomes are compared to other traditional approaches of validation. The contrast with traditional approaches shows that the proposed model provides better outcomes than in traditional models and is therefore a good fit for real-time threat prediction.