CNN-LSTM-based IDS on Precision Farming for IIoT data

Rajak, Prince; Lachure, Jaykumar; Doriya, Rajesh

doi:10.1109/icccmla56841.2022.9988997

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…By leveraging the efficacy of Convolutional Neural Networks (CNNs), there are abundance attempts to demonstrate the feasibility of CNN based algorithm to achieve good results for SFA task [ 7 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Ref.…”

Section: Introductionmentioning

confidence: 99%

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

Sigalingging

Prakosa

Leu

et al. 2023

Sensors

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In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia.

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Section: Introductionmentioning

confidence: 99%

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

Sigalingging

Prakosa

Leu

et al. 2023

Sensors

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An Anomaly Detection Framework for IIoT in Agriculture

Swate,

Sithungu,

Lebea

2024

Proceedings of Ninth International Congress on Information and Communication Technology

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An Attention-Based Convolutional Neural Network for Intrusion Detection Model

Wang

Ghaleb

2023

IEEE Access

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Network technology has had a distinctive impact on the entire human civilization and has become an important factor of production in many countries and regions. However, with the widespread popularity of network technology, security flaws have been scattered in various fields, and potential crises may break out by attackers at any time. Therefore, it is crucial to establish a traffic monitoring mechanism for network systems. Some researchers have already implemented intrusion detection models by convolutional neural networks (CNNs) combined with attention mechanisms and achieved good results. However, few attempts have been made to improve the computational efficiency of the model by organizing the appropriate image structure, and the integration of attention mechanisms could be further enhanced. In this study, an attention-based CNN intrusion detection model has been proposed. Together with the image generation methods described in this paper, an efficient and accurate processing flow is formed. To optimize the use of the feature information in the experiments, the feature fields in the experimental images were arranged according to the results of their importance analysis. And a more integrated attention mechanism has been applied to the CNN for building the detection model. A series of comparative experiments were conducted on a subset of the CSE-CIC-IDS2018 dataset, and the results show that the detection process and model proposed in this paper can swiftly complete the detection procedure while maintaining high accuracy.

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CNN-LSTM-based IDS on Precision Farming for IIoT data

Cited by 5 publications

References 8 publications

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

An Anomaly Detection Framework for IIoT in Agriculture

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

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