An efficient deep learning-based solution for network intrusion detection in wireless sensor network

Sharma, Hanjabam Saratchandra; Sarkar, Arindam; Singh, Moirangthem Marjit

doi:10.1007/s13198-023-02090-0

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Sharma et al (19) suggested a new IDS, concentrating on the wireless sensor network, that incorporates the operational and developmental frameworks. Features are extracted and categorized as part of the proposed DevOps-based intrusion detection approach.…”

Section: Related Workmentioning

confidence: 99%

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

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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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.

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Section: Related Workmentioning

confidence: 99%

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

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

Anusha,

Tapas Bapu,

et al. 2024

Multimed Tools Appl

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A Novel Machine Learning Strategy for Anomaly Identification Scheme in Wireless Sensor Networks Using Supervised Training Principles

Shifani,

Mary,

Metilda

et al. 2024

2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)

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An efficient deep learning-based solution for network intrusion detection in wireless sensor network

Cited by 5 publications

References 28 publications

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

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

Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

A Novel Machine Learning Strategy for Anomaly Identification Scheme in Wireless Sensor Networks Using Supervised Training Principles

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