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With the rapid progress and significant successes in various applications, machine learning has been considered a crucial component in the Internet of Things ecosystem. However, machine learning models have recently been vulnerable to carefully crafted perturbations, so-called adversarial attacks. A capable insider adversary can subvert the machine learning model at either the training or testing phase, causing them to behave differently. The vulnerability of machine learning to adversarial attacks becomes one of the significant risks. Therefore, there is a need to secure machine learning models enabling the safe adoption in malicious insider cases. This paper reviews and organizes the body of knowledge in adversarial attacks and defense presented in IoT literature from an insider adversary point of view. We proposed a taxonomy of adversarial methods against machine learning models that an insider can …