利用机器学习和区块链集成缓解物联网中的 DDoS

Ammar Ibrahim El Sayed;Mahmoud Abdelaziz;Mohamed Hussein;Ashraf D. Elbayoumy
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摘要

物联网(IoT)带来了灵活的数据管理和监控,但也越来越容易受到分布式拒绝服务(DDoS)攻击。为了应对这些威胁并增强物联网设备的信任和计算能力,我们提出了一种创新解决方案,将机器学习(ML)技术与区块链作为支持框架进行整合。通过分析物联网流量数据集,我们发现了 DDoS 攻击的存在,突出了对强大防御的需求。在对多个 ML 模型进行评估后,我们选择了最有效的模型,并将其与区块链相结合,以增强对 DDoS 威胁的检测和缓解,从而加强物联网网络的安全性。这种方法增强了设备的恢复能力,为物联网安全领域做出了巨大贡献。
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DDoS Mitigation in IoT Using Machine Learning and Blockchain Integration
The Internet of Things (IoT) has brought about flexible data management and monitoring, but it is increasingly vulnerable to distributed denial-of-service (DDoS) attacks. To counter these threats and bolster IoT device trust and computational capacity, we propose an innovative solution by integrating machine learning (ML) techniques with blockchain as a supporting framework. Analyzing IoT traffic datasets, we reveal the presence of DDoS attacks, highlighting the need for robust defenses. After evaluating multiple ML models, we choose the most effective one and integrate it with blockchain for enhanced detection and mitigation of DDoS threats, reinforcing IoT network security. This approach enhances device resilience, presenting a promising contribution to the secure IoT landscape.
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Table of Contents IEEE Networking Letters Author Guidelines IEEE COMMUNICATIONS SOCIETY IEEE Communications Society Optimal Classifier for an ML-Assisted Resource Allocation in Wireless Communications
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