# Data security storage and transmission framework for AI computing power platforms

## TL;DR

This paper introduces a secure framework for AI computing platforms that protects data during storage and transmission while minimizing computational overhead.

## Contribution

A novel hybrid encryption and deep learning-based intrusion detection system for secure AI data handling with minimal overhead.

## Key findings

- The Secure AI-DST system reduces unauthorized access attempts by 92.7%.
- It maintains data integrity with 99.98% accuracy under simulated cyberattacks.
- The system achieves a 97.6% packet validation success rate across edge-to-cloud transmissions.

## Abstract

In the era of rapidly expanding artificial intelligence (AI) applications, ensuring secure data storage and transmission within AI computing power platforms remains a critical challenge. This research presents a novel data security storage and transmission system, termed as secure artificial intelligence data storage and transmission (Secure AI-DST), tailored for AI computing environments. The proposed framework integrates a hybrid encryption mechanism that combines Amended Merkle Tree (AMerT) hashing with Secret Elliptic Curve Cryptography (SEllC) enhanced data confidentiality. For secure storage and decentralization, the system leverages blockchain with InterPlanetary File System (IPFS) integration, ensuring tamper-proof and scalable data handling. To classify various attack types, a novel deep learning model attention bidirectional gated recurrent unit-assisted residual network (Att-BGR) is deployed, offering accurate detection of intrusions. Simulation studies conducted in MATLAB® 2023b using both synthetic and real-time datasets show that the Secure AI-DST system reduces unauthorized access attempts by 92.7%, maintains data integrity with 99.98% accuracy under simulated cyberattacks, and achieves a packet validation success rate of 97.6% across edge-to-cloud transmissions. Furthermore, the proposed method introduces only a 4.3% computational overhead, making it highly suitable for real-time AI workloads. These outcomes confirm the effectiveness of Secure AI-DST in ensuring end-to-end data guard, resilience against cyber threats, and scalable presentation for next-generation AI computing substructures.

The online version contains supplementary material available at 10.1038/s41598-025-31786-5.