在物联网环境中利用区块链进行网络安全检测
在PubMed上查看摘要
概括
此摘要是机器生成的。这项研究引入了一种利用区块链和人工智能增强物联网 (IoT) 网络安全的新方法. 该模型显著提高了网络攻击检测率,为物联网网络提供了强大的安全性.
科学领域
- 计算机科学
- 网络安全
- 人工智能
背景情况
- 物联网 (IoT) 应用中的集中存储引发了严重的安全和机密问题.
- 传统的物联网安全措施不足以应对不断发展的网络威胁.
- 区块链和人工智能,特别是深度学习和机器学习,为下一代物联网安全提供了有前途的解决方案.
研究的目的
- 提出一种新的利用区块链进行网络安全检测,使用金优化 (LBCCD-GJO) 方法来增强物联网安全.
- 改善物联网环境中网络攻击的检测和分类.
- 解决物联网网络中传统安全方法的局限性.
主要方法
- 使用min-max规范化进行数据预处理.
- 使用金优化 (GJO) 模型进行特征选择.
- 网络攻击分类采用Gated Recurrent Unit (GRU) 技术,通过草原犬优化和差异演变 (PDO-DE) 的混合优化进行超参数优化.
主要成果
- 拟议的LBCCD-GJO方法在TON_IoT_Train_Test_Network数据集上显示了99.67%的高实验验证准确性.
- 这种方法有效地整合了区块链和人工智能,
- 与以前的技术相比,网络攻击检测有显著的改善.
结论
- 对于物联网系统的网络安全检测,LBCCD-GJO方法提供了一个高度有效和准确的解决方案.
- 区块链,GJO,GRU和PDO-DE的整合为增强物联网安全提供了强大的框架.
- 这项研究证实了先进的人工智能和区块链技术在保护关键物联网应用中的潜力.
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