Data Integrity Attacks against Traffic Modeling and Forecasting in M2M Communications

Internet of Things (IoT) communications include an exceptional number of Machine-to-Machine (M2M) devices to enable automation in smart-world systems. Given the explosion in number of distributed computing devices, traffic modeling and forecasting (TMF) schemes, which imitate and predict device traffic dynamics in M2M communications, become critical to providing useful guidance for constrained network resource planning and scheduling. In this paper, we explore the vulnerability of TMF, particularly focusing on data integrity attacks. Specifically, we consider a generic attack with adversaries impersonating M2M networks to send fake triggering messages to detached M2M devices. Thus, the number of attached M2M devices can be manipulated. We additionally conduct threat modeling and investigate attack impacts on TMF. Our experimental results show that data integrity attacks are effective in disrupting TMF to reduce goodness-of-fit and prediction accuracy.