区块链增强了物联网中使用深度学习和进化计算的分布式拒绝服务检测
V V S H Prasad1, Swathi Sowmya Bavirthi2, C S S Anupama3
1Department of Mechanical Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India.
Scientific reports
|July 1, 2025
概括
本研究介绍了一种新的Metaheuristic优化区块链框架,用于使用深度学习模型 (MOBCF-ADDLM) 检测攻击,以打击物联网 (IoT) 环境中的分布式拒绝服务 (DDoS) 威胁. 拟议的方法达到99.22%的准确性,显著提高物联网安全性.
科学领域:
- 网络安全 网络安全
- 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) 面临着重大安全挑战,特别是分布式拒绝服务 (DDoS) 攻击,由于资源有限的设备.
- 区块链技术 (BC) 为物联网漏洞提供了潜在的分散安全解决方案.
- 现有的方法难以有效地检测和减轻复杂物联网生态系统中的DDoS威胁.
研究的目的:
- 通过先进的技术,提出一种有效的方法来检测物联网环境中的DDoS威胁.
- 通过利用区块链和深度学习来提高物联网应用程序的安全性.
- 通过使用深度学习模型 (MOBCF-ADDLM) 引入用于攻击检测的元启发优化区块链框架.
主要方法:
- 利用区块链技术来实现针对DDoS攻击的分散安全解决方案.
- 实现数据预处理的min-max缩放和特征选择 (FS) 的Aquila优化器 (AO).
- 使用深度信念网络 (DBN) 来进行攻击分类,并通过红熊猫优化器 (RPO) 进行超参数优化.
主要成果:
- MOBCF-ADDLM方法在检测DDoS威胁方面表现出卓越的性能.
- 在BoT-IoT二进制和多类数据集上进行的实验验证了该模型的有效性.
- 提出的方法实现了高精度99.22%,超过了现有模型的性能.
结论:
- MOBCF-ADDLM框架为物联网系统中DDoS攻击检测提供了有效和准确的解决方案.
- 区块链,深度学习和元启发性优化的集成显著提高了物联网安全性.
- 这项研究有助于在不断扩大的物联网环境中对普遍的网络威胁提供强大的防御机制.
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