False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics

False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics Gautam Raj Mode, Prasad Calyam, Khaza Anuarul Hoque Department of Electrical Engineering & Computer Science University of Missouri, Columbia, MO, USA gmwyc@mail.missouri.edu, Abstract --Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms (especially deep learning algorithms) and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. T o date, the majority of the published literature focuses on the accuracy of the IoT and DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks (in the form of false data injection attack) on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-T erm Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. Our obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model and apply two different types of false data injection attacks (FDIA), specifically, continuous and interim FDIAs on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. Our results demonstrate that attacks on even a small number of IoT sensors can strongly defect the RUL prediction in all cases. However, the GRU-based PdM model performs better in terms of accuracy and FDIA resiliency. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths.