@article{uneatlantico3010,
           month = {Junio},
           pages = {6442},
          volume = {12},
          author = {Ankita Anand and Shalli Rani and Aman Singh and Dalia H. Elkamchouchi and Irene Delgado Noya},
          number = {13},
            year = {2022},
           title = {Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT},
         journal = {Applied Sciences},
        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.},
             url = {http://repositorio.uneatlantico.es/id/eprint/3010/},
        keywords = {industrial internet of things; deep learning; security; attacks; privacy}
}