A Comparative Performance Analysis of Dna-Encoded Kamla Approaches in Metagraphy-Based Cryptosystems

Abstract

Modern cryptographic systems face dual pressures of maintaining robust security while delivering acceptable performance across diverse computing environments. Metagraphy-based cryptosystems that integrate DNA encoding with KAMLA (Key Authenticated Message with Lightweight Authentication) protocols promise enhanced security through biological complexity, yet practical deployment depends critically on performance characteristics. This research conducts comprehensive comparative performance analysis of DNA-encoded KAMLA approaches within metagraphic frameworks, evaluating computational efficiency, memory utilization, scalability, and throughput across various implementation strategies. We examine five distinct DNA-KAMLA architectural variants including sequential processing, parallel encoding, hybrid optimization, adaptive selection, and resource-aware implementations, measuring their performance against traditional cryptographic baselines under realistic operational conditions. Through systematic benchmarking across different message sizes, hardware configurations, and workload patterns, we quantify performance tradeoffs inherent in biological cryptography. Our findings reveal that optimized DNA-KAMLA implementations achieve 42-68 MB/s throughput depending on configuration, representing 65-74% performance reduction compared to AES-based systems, while consuming 2.8-4.3 times more memory and CPU resources. However, parallel processing architectures recover significant performance, achieving up to 89 MB/s on multi-core systems. The research demonstrates that DNA-KAMLA approaches remain computationally viable for applications where security margins justify performance costs, particularly when hardware acceleration or parallel processing capabilities are available. This work provides practical guidance for system architects balancing security requirements against performance constraints in metagraphic cryptosystem deployments.