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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Original
Published: 2024-05-13
DOI: https://doi.org/10.56294/sctconf2024762

A Hybrid Rider Optimization with Deep Learning Driven Intrusion Detection Farmwork in Wireless Sensor Network

By
K Sedhuramalingam ,
K Sedhuramalingam

Assistant Professor, Electronics and Communication Engineering, Arjun College of Technology, Coimbatore, Tamil Nadu, India

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Dr.N Saravana Kumar ,
Dr.N Saravana Kumar

Associate Professor, Electronics and Communication Engineering, Dr. Mahalingam college of Engineering and Technology, Coimbatore, Tamil Nadu, India

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Abstract

Introduction: An array of hazards currently exists in cyberspace, prompting extensive research to tackle these concerns. Intrusion Detection Systems (IDS) are a mechanism used to provide security in Wireless Sensor Networks (WSN). The IDS continue to encounter significant challenges in accurately identifying unknown attacks. Conventional Intrusion Detection Systems (IDS) commonly rely on Deep Learning (DL) algorithms, which utilise binary classifiers to classify attacks. The data dimension attribute is affected inside large-scale high-dimensional data sets.
Methods: This research introduces a hybrid GFSO (HGFSO) model combined with Deep Learning Driven Intrusion Detection (HGFSO-DLIDS) to tackle this problem. The HGFSO approach is developed by merging the parameter selection methods of the Felis Margarita Swarm Optimisation (FMSO), the Grampus optimisation algorithm (GOA), and the Deep Convolutional Neural Network (DCNN) with BiLSTM (Bidirectional Long Short-Term Memory) algorithm.
Results: The model training utilised real-time traffic statistics, including the KDDCup 99 and WSN-DS datasets. After being trained and validated using the datasets, the model's performance is assessed by multi-class classification, achieving accuracy rates of 99.89% and 99.64% respectively.
Conclusion: As a result, this occurrence leads to a decrease in the overall effectiveness of detecting assaults. Deep learning may enhance the creation of an intrusion detection system by eliminating complex features in the raw data, resulting in a more precise classification method.

Keywords:

WSN (Wireless Sensor Network),
IDS (Intrusion Detection System),
DL (Deep learning),
Hybrid Optimization,
DCNN (Deep Convolutional Neural Network) and BiLSTM (Bidirectional Lon Short Term Memory),

How to Cite

1.
Sedhuramalingam K, Saravana Kumar D. A Hybrid Rider Optimization with Deep Learning Driven Intrusion Detection Farmwork in Wireless Sensor Network. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 May 13 [cited 2024 Jul. 5];3:762. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/762

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 3 (2024)
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