Optimized Insider Threat Classification and Secure Cookie File Transfer Using SHA-3 Merkle Tree and CNN-LSTM Hybrid Models

Abstract

Background Information : This is the most significant threat to the security of organizational data, namely insiders. The latest techniques are becoming more and more challenged in terms of precision and scalability. A hybrid architecture that combines SHA-3 Merkle Tree and CNN-LSTM offers a resilient solution for the secure transmission of the cookie file and insider threat classification. Objectives: The framework aims to improve the accuracy of insider threat detection, guarantee secure data communication, minimize false positives and negatives, and provide real-time scalability in applications in dynamic environments. Methods: The system uses SHA-3 Merkle Tree for data integrity verification and the CNN-LSTM hybrid model with an attention mechanism for the classification of insider threats, with efficiency and robustness guaranteed. Empirical Results : Accuracy of 97.8 % Slightly at the price of a relatively quite small false positive and false negative (2.7% and 2.3% respectively); with great scaling performance, around 92.5%. Conclusion: Hybrid architecture happens with great security and efficiency towards the detection of insider threats as well as the secure file transfer, thus fitting well in the real time applications. Future scope can be seen through adaptive learning mechanisms and quantum resistant cryptography mechanisms.