A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks
A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks
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DOI：
10.1109/jiot.2020.2996590
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发表时间：
2021-06-15
影响因子：
10.6
通讯作者：
Choo, Kim-Kwang Raymond
中科院分区：
文献类型：
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作者：
Alkadi, Osama;Moustafa, Nour;Choo, Kim-Kwang Raymond
在将区块链和入侵检测结合起来以分别改善数据隐私和检测现有和新兴的网络攻击方面已经进行了大量研究。在这些方法中，基于学习的集成模型可以促进复杂恶意事件的识别，同时确保数据隐私。此类模型还可用于在云中虚拟机 (VM) 实时迁移期间提供额外的安全和隐私保证，并保护物联网 (IoT) 网络。这将允许虚拟机在数据中心或云提供商之间实时安全传输。本文提出了一种深度区块链框架（DBF），旨在通过物联网网络中的智能合约提供基于安全的分布式入侵检测和基于隐私的区块链。入侵检测方法采用双向长短期记忆（BiLSTM）深度学习算法来处理顺序网络数据，并使用UNSW-NB15和BoT-IoT的数据集进行评估。基于隐私的区块链和智能合约方法是使用以太坊库开发的，为分布式入侵检测引擎提供隐私。将 DBF 框架与同行隐私保护入侵检测技术进行比较，实验结果表明 DBF 优于其他竞争模型。该框架有潜力用作决策支持系统，可以帮助用户和云提供商及时、可靠地安全迁移数据。
There has been significant research in incorporating both blockchain and intrusion detection to improve data privacy and detect existing and emerging cyberattacks, respectively. In these approaches, learning-based ensemble models can facilitate the identification of complex malicious events and concurrently ensure data privacy. Such models can also be used to provide additional security and privacy assurances during the live migration of virtual machines (VMs) in the cloud and to protect Internet-of-Things (IoT) networks. This would allow the secure transfer of VMs between data centers or cloud providers in real time. This article proposes a deep blockchain framework (DBF) designed to offer security-based distributed intrusion detection and privacy-based blockchain with smart contracts in IoT networks. The intrusion detection method is employed by a bidirectional long short-term memory (BiLSTM) deep learning algorithm to deal with sequential network data and is assessed using the data sets of UNSW-NB15 and BoT-IoT. The privacy-based blockchain and smart contract methods are developed using the Ethereum library to provide privacy to the distributed intrusion detection engines. The DBF framework is compared with peer privacy-preserving intrusion detection techniques, and the experimental outcomes reveal that DBF outperforms the other competing models. The framework has the potential to be used as a decision support system that can assist users and cloud providers in securely migrating their data in a timely and reliable manner.