Sensor based cyber attack detections in critical infrastructures using deep learning algorithms

Abstract

The technology that has evolved with innovations in the digital world has also caused an increase in many security problems. Day by day the methods and forms of the cyberattacks began to become complicated, and therefore their detection became more difficult. In this work we have used the datasets which have been prepared in collaboration with Raymond Borges and Oak Ridge National Laboratories. These datasets include measurements of the Industrial Control Systems related to chewing attack behavior. These measurements include synchronized measurements and data records from Snort and relays with the simulated control panel. In this study, we developed two models using this datasets. The first is the model we call the DNN Model which was build using the latest Deep Learning algorithms. The second model was created by adding the AutoEncoder structure to the DNN Model. All of the variables used when developing our models were set parametrically. A number of variables such as activation method, number of hidden layers in the model, the number of nodes in the layers, number of iterations were analyzed to create the optimum model design. When we run our model with optimum settings, we obtained better results than related studies. The learning speed of the model has 100\% accuracy rate which is also entirely satisfactory. While the training period of the dataset containing about 4 thousand different operations lasts about 90 seconds, the developed model completes the learning process at the level of milliseconds to detect new attacks. This increases the applicability of the model in real world environment.