Network intrusions are becoming more and more sophisticated to detect. To mitigate this issue, intrusion detection systems (IDSs) have been widely deployed in identifying a variety of attacks and collaborative intrusion detection networks (CIDNs) have been proposed which enables an IDS to collect information and learn experience from other IDSs with the purpose of improving detection accuracy. A CIDN is expected to have more power in detecting attacks such as denial-of-service (DoS) than a single IDS. In real deployment, we notice that each IDS has different levels of sensitivity in detecting different types of intrusions (i.e., based on their own signatures and settings). In this paper, we propose a machine learning-based approach to assign intrusion sensitivity based on expert knowledge and design a trust management model that allows each IDS to evaluate the trustworthiness of others by considering their detection sensitivities. In the evaluation, we explore the performance of our proposed approach under different attack scenarios. The experimental results indicate that by considering the intrusion sensitivity, our trust model can enhance the detection accuracy of malicious nodes as compared to existing similar models.