Detection of Random False Data Injection Cyberattacks in Smart Water Systems Using Optimized Deep Neural Networks

Abstract

A cyberattack detection model based on supervised deep neural network is proposed to identify random false data injection (FDI) on the tank’s level measurements of a water distribution system. The architecture of the neural network, as well as various hyper-parameters, is modified and tuned to acquire the highest detection performance using the smallest size of training data set. The efficacy of the proposed detection model against various activation functions including sigmoid, rectified linear unit, and softmax is examined. Regularization and momentum techniques are applied to update the weights and prohibit overfitting. Moreover, statistical metrics are presented to evaluate the performance and effectiveness of the proposed model in the presence of a range of measurement noise levels. The proposed model is tested for three attack scenarios composed for the battle of the attack detection algorithms. Results confirm that the size of the data sets required to train the neural network (NN) to accomplish the highest levels of accuracy and precision is significantly decreased as the number of hidden layers is increased. The trained 4- and 5-layer deep neural networks are able to detect the readings’ FDIs with 100% precision and accuracy in the presence of 30% background noise in the sensory data.