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Improved Adaptive Spiral Seagull Optimizer for Intrusion Detection and Mitigation in Wireless Sensor Network

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Abstract

A system that leverages blockchain technology to protect network data and provide tamper-proof administration, privacy, and intrusion detection for sensor networks. This blockchain technology takes advantage of the decentralised and open nature of blockchain technology to address the issues of security risks and data privacy concerns in sensor networks. This research put out a cutting-edge method for sensor network intrusion detection and mitigation combining deep reinforcement learning (DRL) and blockchain technology. There are several stages to the suggested method for wireless sensor network intrusion detection. First, using data cleaning and transformation techniques, the datasets (NSL-KDD, CSE-CIC-IDS2018) are gathered and pre-processed. For the intrusion detection task, pertinent characteristics are chosen, including statistical features, protocol-based features, higher-order statistical features (HOS), and Pearson Correlation Based Principal Component Analysis (PC-PCA). To prevent unauthorised access, the best features are encrypted with the latest AES. Following storage in the blockchain network, the encrypted data is guaranteed for integrity, immutability, and transparency. The chosen ideal features are input to the multi-layer perceptron’s (MLP) recurrent neural network (RNN) during the intrusion detection phase. To increase detection accuracy, the weight function of the RNN is adjusted using the Adaptive Spiral Seagull Optimisation (ASSO). The blockchain network takes the appropriate steps to mitigate the attack (BAIT) if an intrusion is discovered. The A* algorithm determines the shortest path for data transmission, and the gateway node uses that path to transfer the encrypted data to the destination node. The destination node receives the encrypted data, decrypts it using the proper decryption method, and then processes it for various applications. Python is used to implement the suggested model.

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Data availability

Data links are available in References [1] and [2].

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Authors and Affiliations

  1. Department of ISE, RV Institute of Technology and Management, Visvesvaraya Technological University, Belagavi, 590018, India

    Swathi Darla

  2. Department of CSE, SJB Institute of Technology, Visvesvaraya Technological University, Belagavi, 590018, India

    C. Naveena

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  1. Swathi Darla
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Correspondence to Swathi Darla.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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Darla, S., Naveena, C. Improved Adaptive Spiral Seagull Optimizer for Intrusion Detection and Mitigation in Wireless Sensor Network. SN COMPUT. SCI. 5, 394 (2024). https://doi.org/10.1007/s42979-024-02725-4

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