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Intrusion detection in internet of things-based smart farming using hybrid deep learning framework

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Abstract

Smart agriculture is a popular domain due to its intensified growth in recent times. This domain aggregates the advantages of several computing technologies, where the IoT is the most popular and beneficial. In this work, a novel and effective deep learning-based framework is developed to detect intrusions in smart farming systems. The architecture is three-tier, with the first tier being the sensor layer, which involves the placement of sensors in agricultural areas. The second tier is the Fog Computing Layer (FCL), which consists of Fog nodes, and the proposed IDS is implemented in each Fog node. The gathered information is transferred to this fog layer for further data analysis. The third tier is the cloud computing layer, which provides data storage and end-to-end services. The proposed model includes a fused CNN model with the bidirectional gated recurrent unit (Bi-GRU) model to detect and classify intruders. An attention mechanism is included within the BiGRU model to find the key features responsible for identifying the DDoS attack. In addition, the accuracy of the classification model is improved by using a nature-inspired meta-heuristic optimization algorithm called the Wild Horse Optimization (WHO) algorithm. The last layer is the cloud layer, which collects data from fog nodes and offers storage services. The proposed system will be implemented in the Python platform, using ToN-IoT and APA-DDoS attack datasets for assessment. The proposed system outperforms the existing methods in accuracy (99.35%), detection rate (98.99%), precision (99.9%) and F-Score (99.08%) for the APA DDoS attack dataset and the achieved accuracy of the ToN-IoT dataset (99.71%), detection rate (99.02%), precision (99.89%) and F-score (99.05%).

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Data availability

The entire implementation of the work will be carried out in the Python platform. The major performance metrics such as accuracy, precision, recall, f-measure and ROC will be computed and compared with the recent techniques relevant to intrusion detection in IoT-enabled smart farming. The data that support this finding of this study are openly available at the following URL/https://www.unb.ca/cic/datasets/ddos-2019.html , https://cloudstor.aarnet.edu.au/plus/s/ds5zW91vdgjEj9i.

Code availability

The code that supports this finding of this study is openly available at the following URL/https://github.com/keerthikethineni/IDin-IOT-based-Smart-Farming-using-HDLF.git

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Acknowledgements

We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

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Authors and Affiliations

  1. Research Scholar, Koneru Lakshmaiah Education Foundation, Guntur, India

    Keerthi Kethineni

  2. Department of Computer Science and Engineering, V.R. Siddhartha Engineering College, Vijayawada, India

    Keerthi Kethineni

  3. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India

    G. Pradeepini

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  1. Keerthi Kethineni
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Contributions

K: conceptualization, Data Curation, Formal Analysis, Investigation, Resources, Software, Writing an original draft. P: Methodology, Project administration, Supervision, Validation, Visualization, Writing-Review & editing, Funding acquisition.

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Correspondence to G. Pradeepini.

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Kethineni, K., Pradeepini, G. Intrusion detection in internet of things-based smart farming using hybrid deep learning framework. Cluster Comput 27, 1719–1732 (2024). https://doi.org/10.1007/s10586-023-04052-4

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  • DOI: https://doi.org/10.1007/s10586-023-04052-4

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