A Collusion Attack Resistance Data Aggregation Scheme in Internet of Things

Data aggregation (DA) plays an important role in the context of Internet of Things (IoT). Although some favorable solutions have been proposed to improve the performances of DA, the complex collusion attacks are often ignored and may produce more serious negative impact on aggregation accuracy. In this article, we design a novel dynamic robust iterative filtering (DRIF) mechanism to enhance the quality of service of IoT applications and improve the vulnerability of DA to the collusion attack. First, the initial reputations based on the maximum likelihood estimation are assigned to sensor nodes in order to resist the collusion attack. Second, the sensor nodes obtain the aggregation result through iterative filtering so as to ensure the accuracy of DA. Especially, a weight updating scheme is proposed to eliminate the negative effect of the accidental anomaly or collusion nodes. Finally, the simulation study indicates that the proposed DRIF mechanism is effective and it can achieve a higher accuracy in the presence of complex dynamic collusion attacks.