The Revolutionizing Cloud Security and Robotics: Privacy-Preserved API Control Using ASLL-LSTM and HAL-LSTM Models with Sixth Sense Technology

Cloud Security and Robotics

Abstract

This study proposes a secure framework for cloud robotics by combining sixth sense technology with ASLL-LSTM (Adaptive Sigmoid LogLog Activation LSTM) and HAL-LSTM (Hierarchical Attention-based Logarithmic LSTM) models. The integration of real-time data processing for robotic activities and privacy-preserved API control addresses key security and privacy concerns. The framework employs Elliptic Curve Cryptography (ECC) for secure communication between robots and the cloud, while adaptive learning models enhance responsiveness and decision-making. With advanced technologies like speech and gesture recognition, the system enables robots to perform safe and connected missions, achieving over 98% accuracy while maintaining low computational overhead. OBJECTIVES: The primary objectives of this research are to develop a secure cloud robotics framework that integrates advanced sixth sense technologies and adaptive learning models, ensuring privacy-preserved API control and secure communication. The framework aims to improve both the operational efficiency and security of robotic systems in cloud-based environments. METHODS: The proposed framework combines ASLL-LSTM and HAL-LSTM models for real-time data processing and adaptive decision-making. It utilizes Elliptic Curve Cryptography (ECC) for secure communication between robots and the cloud, and integrates sixth sense technologies like speech and gesture recognition to enhance robotic interactions. RESULTS: Performance evaluations demonstrate the framework's efficiency, achieving over 98% accuracy in robotic operations with minimal computational load. The system enhances both security and responsiveness, enabling robots to perform safe, connected missions in real-time while addressing key privacy concerns. CONCLUSION: This secure cloud robotics framework, leveraging sixth sense technology and advanced LSTM models, significantly improves operational efficiency and security. The integration of real-time data processing, adaptive learning, and ECC-based secure communication makes it a robust solution for cloud-based robotic applications, enhancing both performance and safety in connected environments.