Research Paper on USE OF DEEP LEARNING ALGORITHMS FOR SENSOR-BASED CYBERATTACK DETECTION IN CRITICAL INFRASTRUCTURES

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Title: USE OF DEEP LEARNING ALGORITHMS FOR SENSOR-BASED CYBERATTACK DETECTION IN CRITICAL INFRASTRUCTURES

Abstract: A rise in security issues has also been brought on by the technological advancements brought about by advancements in the digital world. Cyberattack techniques and formats are becoming more intricate every day, making it harder to detect them. We used datasets created in collaboration with the Raymond Borges and Oak Ridge National Laboratories for this project. These datasets contain measurements of chewing attack behavior from Industrial Control Systems. These measurements consist of relays with a simulated control panel, synchronized measurements, and data records from Snort. Using these datasets, we created two models for this study. The first model is one we refer to as the DNN model, and it was created using the most recent deep learning algorithms. The Autoencoder structure was added to the DNN model to produce the second model. The variables that we used to build our models were all parametrically set. To develop the best model design, a number of factors were examined, including the activation method, the number of hidden layers in the model, the number of nodes in the layers, and the number of iterations. We got better results than those found in related studies when we ran our model with the best settings. The model’s 100% accuracy rate for learning speed is also wholly acceptable. While the learning process for the developed model is completed at a level of milliseconds to detect new attacks, the training period for the dataset with approximately 4 thousand different operations lasts for about 90 seconds. This broadens the model’s scope for use in practical settings.

Keywords: engineering, critical infrastructure, industrial systems, information security, cyber security, cyberattack detections

Paper Quality: SCOPUS / Web of Science Level Research Paper

Paper type: Analysis Based Research Paper

Subject: Computer Science

Writer Experience: 20+ Years

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