HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

Abstract

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 proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL.

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

“…Lilhore et al 45 2023 It is capable of detecting anomalous behavior in complex and evolving environments.…”

“…The security implementation phase helps to deploy the secure network infrastructure across multiple devices and systems. Lilhore et al 45 have discussed how the model is optimized by using convolutional neural network (CNN) and long short‐term memory (LSTM) architectures and transfer learning techniques. The optimized model accurately detects abnormal network behavior without relying on massive computational resources or additional data collection.…”

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

“…At the heart of our NIDS framework lies a DL model that operates in two critical dimensions: spatial feature extraction and temporal sequence processing. First, the CNN layers effectively capture spatial dependencies within individual data packets [27]. This process uses convolutional filters that slide across the input data to identify crucial features such as specific packet sizes or unusual protocol behavior that could signify an intrusion attempt.…”

Cloud Computing Security via Intelligent Intrusion Detection Mechanisms

“…PaaS builds on IaaS by offering technical tiers and management software instances, freeing developers to focus on creating apps rather than managing the underlying infrastructure. SaaS, on the other hand, does away with the need for local program installation by giving users access to fully functional software that can be accessed over the cloud [2][3][4][5][6][7].…”