Secure Transactions in IoT Network Using PINN-Based Intrusion Detection System and HDPoA Blockchain Protocol

IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS
Sourav Mishra, Vijay Kumar Chaurasiya
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引用次数: 0

Abstract

IoT is a rapidly developing technology with a wealth of creative application possibilities. However, IoT wireless sensor networks are vulnerable to Denial of Service (DoS) attacks due to their insecure nature. Although network integrity and security have been ensured through the use of distributed ledger and blockchain technologies, privacy preservation concerns frequently arise with traditional approaches. So, deep learning-based Physics-informed neural networks (PINN) and Honesty-based Distributed Proof-of-Authority (HDPoA) are developed to enhance transaction security and detect intrusions. Initially, the mobile nodes were deployed in different regions to gather transactions and an intrusion detection system to analyze attacks. First, the Intrusion Detection System (IDS) uses a deep learning approach for detecting the intrusion in the network. For that, the collected data from the deployed nodes are pre-processed using Variational auto-encoder and min-max normalization to standardize input dataset values. Then the features are selected using wild horse optimization and classified using PINN to predict data attack or non-attack. After that, Homomorphic variable tag generation is used for normal transactions with multiple copies in the same document, which are then converted into hash values using the Keccak hashing function. The miner validates transactions based on rank-based priority. Honesty-based Distributed Proof-of-Authority (HDPoA) was used for network security, making it suitable for deployment in blockchain-based IoT applications. The proposed deep learning-based PINN classifier reached 97.2% accuracy and 96.52% specificity. Homomorphic variable (HV) tag generation takes 0.4 s, while the Keccak algorithm takes 0.3 s for hash generation, and the HDPoA protocol has 420 s for block generation time.

基于pup入侵检测系统和HDPoA区块链协议的物联网安全交易
物联网是一项快速发展的技术,具有丰富的创造性应用可能性。然而,物联网无线传感器网络由于其不安全的性质,容易受到拒绝服务(DoS)攻击。尽管通过使用分布式账本和区块链技术确保了网络的完整性和安全性,但传统方法经常出现隐私保护问题。因此,基于深度学习的物理信息神经网络(PINN)和基于诚实的分布式权威证明(HDPoA)被开发出来,以增强交易安全性和检测入侵。最初,移动节点被部署在不同的地区来收集交易,并使用入侵检测系统来分析攻击。首先,入侵检测系统(IDS)使用深度学习方法来检测网络中的入侵。为此,从部署节点收集的数据使用变分自编码器和最小-最大归一化进行预处理,以标准化输入数据集值。然后使用野马优化选择特征,并使用PINN进行分类,预测数据攻击或非攻击。之后,同态变量标记生成用于具有同一文档中多个副本的正常事务,然后使用Keccak散列函数将其转换为散列值。矿工根据基于等级的优先级验证交易。基于诚实的分布式权威证明(HDPoA)用于网络安全,使其适合部署在基于区块链的物联网应用程序中。所提出的基于深度学习的PINN分类器准确率达到97.2%,特异性达到96.52%。同态变量(HV)标签的生成时间为0.4 s, Keccak算法的哈希生成时间为0.3 s, HDPoA协议的块生成时间为420 s。
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来源期刊
CiteScore
8.90
自引率
13.90%
发文量
249
期刊介绍: ransactions on Emerging Telecommunications Technologies (ETT), formerly known as European Transactions on Telecommunications (ETT), has the following aims: - to attract cutting-edge publications from leading researchers and research groups around the world - to become a highly cited source of timely research findings in emerging fields of telecommunications - to limit revision and publication cycles to a few months and thus significantly increase attractiveness to publish - to become the leading journal for publishing the latest developments in telecommunications
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