Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain

Sajid, Muhammad; Ullah, Sameeh; Javaid, Nadeem; Ullah, Ibrar; Qamar, Ali Mustafa; Zaman, Fawad

doi:10.1155/2022/7386049

Cited by 17 publications

(17 citation statements)

References 57 publications

(53 reference statements)

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“…The lifetime of the first node is the smallest for both LEACH and HMGEAR, and the largest for LEACH-RMCV and HMGEAR-RMCV. The reason is that the proposed clustering method effectively uses the network's total energy and ulti- That is why nodes consume less energy and have less computational cost as given in [5], [52]. Ropsten environment is used for PoW while Rinkeby is used for PoA [53].…”

Section: Simulation Resultsmentioning

confidence: 99%

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A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Khan

Amjad

Ahmed³

et al. 2023

IEEE Access

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Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-ofwork in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Blockchain is an emerging technology that is used in industries and other domains. This technology is useful in the area of energy, education, health, etc., [5], [52]. However, it is still vulnerable to various attacks.…”

Section: Security Analysismentioning

confidence: 99%

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Khan

Amjad

Ahmed³

et al. 2023

IEEE Access

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“…After calculation of trust of each message, we come to know that these nodes send less trustworthy data packets and are considered as malicious. For performing secure routing and successfully sending all the packets to the destination, malicious nodes are detected and revoked from the network [35].…”

Section: Malicious Nodes' Detectionmentioning

confidence: 99%

“…The security analysis is performed through the Oyente tool that detects the attacks, which is possible on the smart contract, as shown in Figure 17. The attacks are discussed as follows [35].…”

Section: Smart Contract Analysismentioning

confidence: 99%

Exploiting Blockchain and RMCV-Based Malicious Node Detection in ETD-LEACH for Wireless Sensor Networks

Khan

Sajid

Rauf

et al. 2022

Wireless Communications and Mobile Computing

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In this paper, a routing protocol based on energy temperature degree-low energy-adaptive clustering hierarchy (ETD-LEACH) is proposed. In the protocol, nodes consume less energy when transmitting data, which improves the network lifetime. The proposed protocol selects the cluster heads (CHs) on the bases of degree, temperature, and energy to perform routing. Moreover, for solving the issue of a single point of failure, the blockchain is utilized. The data transactions are also housed in the blockchain, which is deployed on the CHs and BSs, as, in blockchain, multiple nodes take part. Therefore, to perform a consensus between them, a proof-of-authority (PoA) consensus mechanism is used in the underlying work. In the blockchain, the secure hashing algorithm-256 (SHA-256) is used for secure hashing of data transactions. Furthermore, malicious nodes are detected during the routing using the real-time message content validation (RMCV) scheme in the ETD-LEACH protocol. The proposed model is evaluated under the denial-of-service (DoS) attack, the man-in-the-middle (MITM) attack, and the smart contract analysis performed by the Oyente tool. The performance of the proposed model is evaluated through simulations. The ETD-LEACH and energy threshold-low energy-adaptive clustering hierarchy (ETH-LEACH) protocols are compared using different parameters like number of alive nodes, energy consumption, throughput, and delay. ETD-LEACH consumes less energy and has a better network lifetime as compared to ETH-LEACH. In addition, the RMCV-ETD-LEACH network performance is better than that of both DoS-ETD-LEACH and MITM-ETD-LEACH. Moreover, PoA transaction cost is less than that of proof of work. Also, the execution time of SHA-256 is less than the execution time of SHA-512. Moreover, the value of the packet delivery ratio (PDR) is found to be 89.9% and 99.9% with and without the malicious nodes, respectively.

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“…The proposed approach is further compared with Mohar et al [70], and Kumar et al [71] performed an average localization error of 0.43 with four beacon nodes using a hybrid scalable, secure collaborative localization technique for WSNs. Sajid et al [72] presented models based on genetic algorithms are presented for identifying malicious nodes, specifically the GA-SVM and GA-DT. After a rogue node has been identified, the Dijkstra algorithm is used to determine the most efficient route through the network.…”

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confidence: 99%

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

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Wireless sensor networks are the core of the Internet of Things and are used in healthcare, locations, the military, and security. Threats to the security of wireless sensor networks built on the Internet of Things (IoT-WSNs) can come from a variety of sources. This study proposes secure attack localization and detection in IoT-WSNs to improve security and service delivery. The technique used blockchain-based cascade encryption and trust evaluation in a hierarchical design to generate blockchain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves data security and transmission by merging raw device data and placing malicious threats in the blockchain. Malicious nodes are classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, K -means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance and security, when utilizing heterogeneous wireless senor networks for the providing of secure services.

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Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain

Cited by 17 publications

References 57 publications

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Exploiting Blockchain and RMCV-Based Malicious Node Detection in ETD-LEACH for Wireless Sensor Networks

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

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