## 2 Related Work  
Collaborative intrusion detection systems/networks are developed to boost the accuracy of a separate detector, which usually has less information about the protected environment. This collaborative network enables various IDS nodes to request and collect data from other nodes. However, the collaborative nature renders it vulnerable to insider attacks, in which intruders are inside the network. To protect distributed systems and collaborative networks against malicious nodes, establishing a proper trust-based intrusion detection mechanism is desirable. Trust-Aware Mechanism  
Trust management has been widely studied in literature. [PERSON] et al. [3] described a P2P-based overlay IDS, which utilizes a trust engine to handle alarms and an adaptive scheme to calculate reputation. More specifically, the former is used to filter out alerts sent by untrusted or low-reputation nodes, while the latter can calculate the reputation of nodes by considering their past behaviors. [PERSON] et al. [11] recently proposed a Bayesian inference-based trust mechanism to identify untruthful nodes for medical smartphone networks. The evaluation showed that their approach could quickly identify malicious nodes in real scenarios. For some other related works, we refer to [12; 13; 14; 15; 16; 17; 18]. How to design an appropriate trust management in CIDNs remains an issue. For this purpose, [PERSON] et al. [5] designed a challenge-based trust mechanism, which sends challenges to evaluate the reputation of an IDS node. The trustworthiness of a node can be derived according to the received answers. At first, they described a detection framework based on HIDSs, in which each HIDS node could judge the trustworthiness of others on the basis of the difference between the sent challenges and the received answers. They further utilized a forgetting factor to emphasize the recent feedback [6]. Then, they enhanced their mechanism with a Dirichlet-based model, which allows for the evaluation of the reputation of IDS nodes by considering their mutual behavioral events [7]. In the evaluation, they mainly evaluated their model for challenge-based CIDNs in some simulated environments. The mechanism was found to have strong scalability properties and to be robust against common insider threats. Advanced Insider Attack  
Current intrusions have become more complex, and many research studies have moved to advanced attacks. [PERSON] et al. [8; 19] developed an advanced collusion attack, named _passive message fingerprint attack_ (PMFA), which allows several malicious nodes to exchange received data and distinguish normal requests passively. Experimental results indicated that the PMFA enabled IDS nodes to give untruthful answers to normal requests without decreasing their trust values. Similarly, [PERSON] et al. [9] also developed an advanced collusion attack, called the _random poisoning attack_, which enables a node to provide malicious answers with a predefined possibility. They performed two experiments under both simulated and real environments, and it was found that this attack could compromise the robustness of challenge-based CIDNs. Mechanism Improvement  
To enhance the mechanism performance, [PERSON] et al. [20] pointed out that distinct IDS nodes may not have the same detection capabilities. Some nodes could have a higher or lower level of sensitivity for the detection of some particular intrusions. As an example, the number of signatures can decide whether an IDS node has a stronger capability of identifying a kind of virus. That is, a node can be more accurate in identifying such a threat if it has a larger set of relevant signatures. On the basis of this observation, they proposed _intrusion sensitivity_ (IS), which could be used to measure the detection sensitivity of an IDS node in terms of particular intrusions. They further proposed a trust managementapproach by means of IS, through automating the allocation of IS with machine learning techniques in real-world applications [21; 22].