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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: 2024-06-20
DOI: https://doi.org/10.56294/sctconf2024934

Enhancing industrial security with iot-based passive intrusion detection and segmentation

By
Arunkumar S. ,
Arunkumar S.

Assistant Professor (SG), Department Of EEE, Nehru Institute of Engineering and Technology, Nehru gardens, Thirumalayampalayam, Coimbatore, Tamilnadu, India

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Gowtham M.S. ,
Gowtham M.S.

Associate Professor, Department of Electronics and Communication Engineering, Karpagam Institute of Technology, Coimbatore, Tamilnadu, India

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Revathi N. ,
Revathi N.

Assistant Professor, Department of ECE, Nehru Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

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Krishnaprasath V.T. ,
Krishnaprasath V.T.

Assistant Professor, Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

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Abstract

Introduction: Passive intrusion detection in industrial environments can be challenging, especially when the area being monitored is vast. However, with the advent of IoT technology, it is possible to deploy sensors and devices that can help with mass segmentation of passive intrusion. Hence, this approach deploys ML (Machine Learning) algorithm as improvised (Convolutional Neural Network) CNN support for identifying and avoid illegal access to critical areas in real time, ultimately improving security and safety in industrial environments. Methods: In turn the proposed algorithm can detect patterns and anomalies that could indicate a passive intrusion. In order to discover the patterns and connections between the various sensor data points, DL (Deep Learning) techniques like CNNs, Recurrent Neural Networks (RNNs), and Autoencoders (AE) may be trained on massive datasets of sensor data.
Results: Then, the robust technique DL (Deep Learning) can be utilized for ID (Intrusion Detection) the industrialized settings, when specifically combined with other IoT devices like sensors and alert systems. Thus, the model is trained and tested. Finally, it achieved 98.51% and 94.85% accuracy accordingly.
Conclusion: These frameworks after the completing training phase can be employed for the novel sensor data’s actual analysis and also for the anomalies detection as it reveals a potential ID.

Keywords:

Convolutional Neural Networks (CNN),
IoT,
Recurrent Neural Networks (RNN),
Passive (ID) Intrusion Detection,

How to Cite

1.
Arunkumar S, Gowtham M, Revathi N, Krishnaprasath V. Enhancing industrial security with iot-based passive intrusion detection and segmentation. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jun. 20 [cited 2024 Jul. 1];3:934. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/934

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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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