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HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning by Umesh Kumar Lilhore; Poongodi Manoharan; Sarita Simaiya; Roobaea Alroobaea; Majed Alsafyani; Abdullah M. Baqasah; Surjeet Dalal; Ashish Sharma; Kaamran Raahemifar is a Engineering article available to read on EtoBox.

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Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model’s prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the propose

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Author

Umesh Kumar Lilhore; Poongodi Manoharan; Sarita Simaiya; Roobaea Alroobaea; Majed Alsafyani; Abdullah M. Baqasah; Surjeet Dalal; Ashish Sharma; Kaamran Raahemifar

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)