  As Internet of Things (IoT) has emerged as the next logical stage of the
Internet, it has become imperative to understand the vulnerabilities of the IoT
systems when supporting diverse applications. Because machine learning has been
applied in many IoT systems, the security implications of machine learning need
to be studied following an adversarial machine learning approach. In this
paper, we propose an adversarial machine learning based partial-model attack in
the data fusion/aggregation process of IoT by only controlling a small part of
the sensing devices. Our numerical results demonstrate the feasibility of this
attack to disrupt the decision making in data fusion with limited control of
IoT devices, e.g., the attack success rate reaches 83\% when the adversary
tampers with only 8 out of 20 IoT devices. These results show that the machine
learning engine of IoT system is highly vulnerable to attacks even when the
adversary manipulates a small portion of IoT devices, and the outcome of these
attacks severely disrupts IoT system operations.
