An improved DV-Hop localization algorithm for wireless sensor networks

Wireless Communications and Mobile Computing

2023

Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

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Section: Related Workmentioning

confidence: 99%

“…This algorithm was first identified by [2]. The distance vector localization scheme is a range-free strategy [19] with a series of steps in Table 2.…”

Section: Cluster Formation and Data Aggregationmentioning

confidence: 99%

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Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Wireless Communications and Mobile Computing

2023

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“…In [34], this algorithm was discovered for the first time. Here are the steps of the range-free distance vector localization scheme [35]: 4.2.1. Routing Initialization.…”

Section: Selection Of Miner Node and Localization Processmentioning

confidence: 99%

“…The beacon node transmits a message to all sensor nodes. As soon as it enters the network, the hop count associated with its position data is set to 0 [35]. A node that gets such a message will determine where the beacon node is located, append its own identifier to the message, and then broadcast it to its neighbours while increasing the hop count [36].…”

Section: Selection Of Miner Node and Localization Processmentioning

confidence: 99%

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

Wireless Communications and Mobile Computing

2023

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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Secure Localization Techniques in Wireless Sensor Networks Against Routing Attacks Using Machine Learning Models

Smart Innovation, Systems and Technologies

2023