最佳ElGamal加密与混合深度学习基于安全物联网环境的分类
Chinnappa Annamalai1, Chellavelu Vijayakumaran1, Vijayakumar Ponnusamy2
1Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.
本研究介绍了物联网 (IoT) 的新型安全模型,通过先进的加密和深度学习分类来增强数据保护. 拟议的方法实现了高精度,确保了物联网环境中的安全数据传输.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 整合了智能设备,增加了社会对互联技术的依赖.
- 物联网安全是由于庞大的网络和敏感数据传输而面临的关键挑战.
- 确保数据完整性和保密性对于安全的物联网系统设计至关重要.
研究的目的:
- 开发和评估用于物联网环境的新型安全模型.
- 在物联网系统中增强数据加密和分类流程.
- 在不断扩大的物联网生态系统中解决对强大安全的关键需求.
主要方法:
- 一个混合模型,将Slime Mold优化与ElGamal加密 (SMOEGE) 结合起来,用于数据加密.
- 使用SMO算法在ElGamal加密技术中生成最佳密钥.
- 实现基于深度学习的混合分类 (HDL) 模型,通过Nadam优化器进行优化,用于数据分类.
主要成果:
- 在实验验证中,SMOEGE-HDL模型表现出卓越的性能.
- 获得了高分:98.50%的特异性,98.75%的精度,98.30%的回忆,98.50%的准确性,98.25%的F1分.
- 进行的比较分析证实了拟议技术比现有方法更有效.
结论:
- SMOEGE-HDL模型在物联网安全方面取得了重大进展.
- 混合方法有效地平衡了数据加密和分类,以提供强大的保护.
- 该模型的高性能指标表明其对现实世界物联网安全应用的潜力.
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