# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 2 Related Work  
Trust management is an effective method to identify malicious, selfish or compromised nodes. In recent years, research on trust management and its application to intrusion detection has received considerable attention from researchers. The current trust evaluation schemes aim to improve the detection performance, resource efficiency, robustness etc., by using fuzzy theory, probability theory and statistics, weighting method, etc. [22]. In [23; 24; 25], fuzzy theory is used to determine the trust degree of a sensor node. [PERSON] et al. [23] proposed a trust evaluation algorithm named as Node Behavioral strategies Banding belief theory of the Trust Evaluation algorithm (NBBTE). In their scheme, each node firstly establishes the direct and indirect trust values of neighboring nodes by comprehensively considering various trust factors and then fuzzy set theory is used to decide the trustworthiness levels of the sensor nodes. Finally, D-S evidence theory method is adopted to obtain an integrated trust value instead of a simple weighted-average one. [PERSON] et al. [24] put forward a trust model to detect anomaly nodes in WSNs based on fuzzy theory and evidence theory. Fuzzy theory is used to calculate the trustworthiness levels of multi-dimensional characteristics of the evaluated node and the evidence theory is applied to integrate a direct trust value for the evaluated node. [PERSON] et al. [25] proposed a lightweight and dependable trust model for clustered wireless sensor network, in which the fuzzy degree of nearness is adopted to evaluate the reliability of the recommended trust values from the third party nodes. In [26; 27], probability distribution is used to build the trust evaluation model. [PERSON] et al. [26] presented a distributed reputation-based framework for sensor networks. It uses a watchdog mechanism to monitor communication behaviors of neighboring nodes, represents node reputation distribution using Beta distribution and calculates the trust value according to the statistical expectation of the probability reputation distribution. [PERSON] et al. [27] proposed a dynamic trust management scheme for WSNs. It uses a hash algorithm to generate identify labels for SNs and builds a trust-evaluating model based on beta density function. In [28; 29; 30; 31; 32; 33], trust is estimated using weighting method. [PERSON] et al. [28] proposed a weighted-trust evaluation based scheme to detect compromised or misbehaved nodes in WSNs by monitoring their reported data. The hierarchical network can reduce the communication overhead between sensor nodes by utilizing clustered topology. [PERSON] et al. [29] presented a group-based trust management scheme for clustered WSNs. It evaluates the trust of a group of nodes in contrast to traditional trust schemes that usually focus on the trust values of individual nodes, which reduces the cost of trust evaluation. [PERSON] et al. [30] put forward a parameterized and localized trust management scheme for WSNs, where each sensor node maintains highly abstracted parameters, rates the trustworthiness of its interested neighbors to adopt appropriate cryptographic methods, identify the malicious nodes, and share the opinion locally. [PERSON] et al. [31] proposed a lightweight and dependable trust system for clustered WSNs. Given the cancellation of feedback between nodes, it can greatly improve system efficiency while reducing the effect of malicious nodes. By adopting a dependability-enhanced trust evaluating approach for cooperation between CHs, it can effectively detect and prevent malicious, selfish and faulty CHs. [PERSON] et al. [32] presented an efficient distributed trust model for WSNs. In their model, the trustworthiness of a node includes direct trust and indirect trust. During the calculation of direct trust, communication trust, energy trust and data trust are considered. When a subject node cannot directly observe object nodal communication behaviors, the indirect trust value is gained based on the recommendations from some other nodes.