eprintid: 3010 rev_number: 11 eprint_status: archive userid: 2 dir: disk0/00/00/30/10 datestamp: 2022-07-27 23:30:11 lastmod: 2023-07-13 23:30:23 status_changed: 2022-07-27 23:30:11 type: article metadata_visibility: show creators_name: Anand, Ankita creators_name: Rani, Shalli creators_name: Singh, Aman creators_name: Elkamchouchi, Dalia H. creators_name: Delgado Noya, Irene creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: creators_id: irene.delgado@uneatlantico.es title: Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: public keywords: industrial internet of things; deep learning; security; attacks; privacy abstract: Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as medicines, automobiles, logistics, etc. However, every field is suffering from some threats and vulnerabilities. In this paper, a new model is proposed for detecting different types of attacks and it is analyzed with a deep learning technique, i.e., classifier-Convolution Neural Network and Long Short-Term Memory. The UNSW NB 15 dataset is used for the classification of various attacks in the field of Industry 4.0 for providing security and protection to the different types of sensors used for heterogeneous data. The proposed model achieves the results using Cortex processors, a 1.2 GHz processor, and four gigabytes of RAM. The attack detection model is written in Python 3.8.8 and Keras. Keras constructs the model using layers of Convolutional, Max Pooling, and Dense Layers. The model is trained using 250 batch size, 60 epochs, 10 classes. For this model, the activation functions are Relu and softmax pooling. date: 2022-06 publication: Applied Sciences volume: 12 number: 13 pagerange: 6442 id_number: doi:10.3390/app12136442 refereed: TRUE issn: 2076-3417 official_url: http://doi.org/10.3390/app12136442 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as medicines, automobiles, logistics, etc. However, every field is suffering from some threats and vulnerabilities. In this paper, a new model is proposed for detecting different types of attacks and it is analyzed with a deep learning technique, i.e., classifier-Convolution Neural Network and Long Short-Term Memory. The UNSW NB 15 dataset is used for the classification of various attacks in the field of Industry 4.0 for providing security and protection to the different types of sensors used for heterogeneous data. The proposed model achieves the results using Cortex processors, a 1.2 GHz processor, and four gigabytes of RAM. The attack detection model is written in Python 3.8.8 and Keras. Keras constructs the model using layers of Convolutional, Max Pooling, and Dense Layers. The model is trained using 250 batch size, 60 epochs, 10 classes. For this model, the activation functions are Relu and softmax pooling. metadata Anand, Ankita; Rani, Shalli; Singh, Aman; Elkamchouchi, Dalia H. y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT. Applied Sciences, 12 (13). p. 6442. ISSN 2076-3417 document_url: http://repositorio.uneatlantico.es/id/eprint/3010/1/applsci-12-06442-v2.pdf