Traditional intrusion detection systems (IDSs) work in isolation and may be easily compromised by new threats. An intrusion detection network (IDN) is a collaborative IDS network intended to overcome this weakness by allowing IDS peers to share collective knowledge and experience, hence improve the overall accuracy of intrusion assessment. In this work we design an incentive model based on trust management by using game theory for peers to collaborate truthfully without free-riding in an IDN environment. We show the existence and uniqueness of a Nash equilibrium under which peers can communicate in an incentive compatible manner. Using duality of the problem, we develop an iterative algorithm that converges geometrically to the equilibrium. Our numerical experiments and discrete event simulation demonstrate the convergence to the Nash equilibrium and the incentives of the resource allocation design.