Confidential Threat Detection Using Vectorized Adaptive Capsule Neural Network for Privacy-Centric Cybersecurity Cloud Framework

The rising adoption of cloud computing has transformed data processing and storage, but it has also posed substantial cybersecurity threats as a result of changing attack patterns and issues of privacy. Maintaining confidentiality and real-time detection of threats is a crucial challenge in extensive cloud setups. Conventional threat detection solutions are dependent on centralized architectures that sacrifice data privacy and are not effective in dealing with high false positive rates. The current federated learning and privacy-preserving approaches encounter issues with handling non-independent and identically distributed (non-IID) data distributions and adversarial attacks which hinders their performance in realistic cloud security deployments. The research develops “Confidential Threat Detection using Vectorized Adaptive Capsule Neural Network for Privacy-Centric Cybersecurity Cloud Framework (CyberCap)” to deliver precise threat classification with privacy safeguards. CyberCap uses Vectorized Adaptive Capsule Neural Network for attack detection which guarantees accuracy through Strategic Warfare Algorithmic Optimization that optimizes loss function parameters and weights. The system implements Revolutionized Swift Hybrid Cryptography for secure data storage and transmission when no attacks are identified. The experimental results indicate CyberCap achieves a detection accuracy rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 9. 8 4 \%}$</tex> and a precision rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 9. 7 6 \%}$</tex> and recall of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 9. 9 2 \%}$</tex> and an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 9. 8 8 \%}$</tex>. The threat detection characteristics of CyberCap provide integrated protection for security across cloud environments through minimal resource consumption while upholding data purity at every stage.