比较用于优化电子商务集成中椭圆曲线密码参数的人工智能算法：前量子时代分析
Comparing AI Algorithms for Optimizing Elliptic Curve Cryptography Parameters in e-Commerce Integrations: A Pre-Quantum Analysis
AI总结：
本文比较遗传算法与粒子群优化在椭圆曲线密码参数优化中的表现，并在模拟电商环境中验证其安全性，为前量子时代第三方集成提供网络安全建议。
摘要：
本文对遗传算法（GA）和粒子群优化（PSO）这两种重要的人工智能算法进行了比较分析，重点在于优化椭圆曲线密码（ECC）参数。这些参数包括椭圆曲线系数、素数、生成元点、群阶和余因子。该研究揭示了在相同适应度函数下，哪种仿生算法能为ECC配置带来更好的优化结果。该适应度函数纳入了确保ECC参数稳健性的方法，包括评估奇异曲线或异常曲线，以及应用Pollard's rho攻击和Hasse定理来提高优化精度。由GA和PSO生成的优化参数在模拟电子商务环境中进行了测试，并在使用椭圆曲线Diffie-Hellman（ECDH）和基于哈希的消息认证码（HMAC）传输订单消息的过程中，与secp256k1等知名曲线进行了对比。本研究聚焦于前量子时代的传统计算，突出了GA和PSO在ECC优化中的有效性，并对增强第三方电子商务集成中的网络安全具有重要意义。我们建议在量子计算广泛采用之前，立即考虑这些发现。
英文摘要：
This paper presents a comparative analysis between the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two vital artificial intelligence algorithms, focusing on optimizing Elliptic Curve Cryptography (ECC) parameters. These encompass the elliptic curve coefficients, prime number, generator point, group order, and cofactor. The study provides insights into which of the bio-inspired algorithms yields better optimization results for ECC configurations, examining performances under the same fitness function. This function incorporates methods to ensure robust ECC parameters, including assessing for singular or anomalous curves and applying Pollard's rho attack and Hasse's theorem for optimization precision. The optimized parameters generated by GA and PSO are tested in a simulated e-commerce environment, contrasting with well-known curves like secp256k1 during the transmission of order messages using Elliptic Curve-Diffie Hellman (ECDH) and Hash-based Message Authentication Code (HMAC). Focusing on traditional computing in the pre-quantum era, this research highlights the efficacy of GA and PSO in ECC optimization, with implications for enhancing cybersecurity in third-party e-commerce integrations. We recommend the immediate consideration of these findings before quantum computing's widespread adoption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
