DATA ENCRYPTION USING BIO-CRYPTO FRAMEWORK INTEGRATING AI & ML MODELS FOR ENHANCING THE CRYPTOGRAPHIC STRENGTH, IMPROVING ADAPTABILITY, ENSURING SECURED DATA TRANSMISSION IN CLOUD COMPUTING DISTRIBUTED ENVIRONMENTS

In this research paper, a study / review of the design & development of a data encryption bio-crypto framework integrating AI & ML models for enhancing the cryptographic strength, improving adaptability, ensuring secured data transmissions in cloud computing distributed net environments is presented. The research work undertaken presents a data-encryption bio-cryptographic framework integrated with AI & ML techniques for secure data transmission in cloud-computing and distributed-network environments. The proposed framework combines biological-sequence-based key material generation, cryptographic extraction, authenticated encryption, adaptive key management, and intelligent threat detection. Biological sequences are converted into binary representations and processed using cryptographic hash functions and key-derivation mechanisms to generate session-specific encryption keys. To avoid the security risks associated with biased or repetitive biological sequences, the framework evaluates source entropy and initiates source refresh or rekeying whenever the measured entropy falls below a predefined threshold. Standard authenticated encryption is retained as the core confidentiality and integrity mechanism, ensuring that biological data are used as an entropy source rather than as an unverified replacement for established cryptographic algorithms. An AI/ML-based adaptation controller monitors network traffic load, latency, packet loss, replay activity, tampering probability, queue occupancy, energy availability, and biological-source quality. The designed system framework could be used for any type of application.