The Internet of Things (IoT) is transforming industries by connecting a myriad of devices to collect and exchange data. However, this connectivity increases vulnerability to cyber threats, making anomaly detection a critical and challenging research topic. Collaborative abnormal detection leverages multiple IoT devices to identify irregularities, but its success hinges on active participation from these devices. This paper explores the development of an efficient incentive mechanism to encourage device participation in collaborative abnormal detection, ensuring robust security in IoT networks. We consider two different knowledge levels of agents and integrate the success detection rate into the utility function of the incentive mechanisms. In particular, we proposed a lightweight method to compute the contribution of agents. Our evaluations reinforce the effectiveness of the proposed methods in enhancing critical performance metrics (by as much as \(14\%\) on average), albeit at the cost of longer convergence times (increased by around \(11\%\) on average).