Wireless sensor network (WSN) nodes owing to their openness, are susceptible to several threats, one of which is dishonest recommendation attacks providing false trust values that favor the attacker. In this letter, a malicious node detection strategy is proposed based on a fuzzy trust model and artificial bee colony algorithm (ABC) (FTM-ABC). The fuzzy trust model (FTM) is introduced to calculate the indirect trust, and the ABC algorithm is applied to optimize the trust model for detecting dishonest recommendation attacks. Besides, fitness function includes recommended deviation and interaction index deviation to enhance the effectiveness. Simulation results reveal the improved FTM-ABC maintains a high recognition rate and a low false-positive rate, even if the number of dishonest nodes reaches 50%.

Wireless sensor network (WSN) works in a complex environment. To interdict the malicious nodes which attacks the safety of network, such as interrupt attacks and selective forwarding attacks, based on TS-BRS reputation model, a model for malicious node identification based on MNRT-OEP&RS algorithm is constructed. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then the similarity of between the benchmark reputation sequence and cycle reputation sequence sets the dynamic reputation double threshold are calculated in order to identify the malicious nodes by dynamically considering the information forwarding behavior. The simulated results show that the improved algorithm can guarantee the security of wireless sensor networks in complex environments effectively with above 90% recognition of malicious nodes and below 8% false positive rate.

Wireless sensor network (WSN) works in a complex environment where it is difficult for people to reach or work. The openness of nodes leads to security threats vulnerable to various attacks. The trust and reputation model can be applied in WSN to reduce damage caused by malicious nodes. However, there is a high false-positive rate in trust and reputation models because a node with less reputation due to the communication environment is judged as a malicious one directly. This paper presents a trust & reputation-based malicious node identification strategy with environmental parameters (TRS&EP) to interdict the malicious nodes, such as interrupt attack nodes and selective forwarding attack nodes. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then TRS&EP estimates benchmark trust according to the environmental parameters. The Gaussian radial basis function is simplified to calculate the similarity between the benchmark trust sequence and cycle reputation sequence. Furthermore, TRS&EP sets three reputation intervals and an adoptive threshold span to identify the malicious nodes by dynamically considering the work environment and states of nodes. The simulated results show that TRS&EP improves the recognition of malicious nodes above 1% compared to comparison algorithms and reduces the false-positive percentage by more than 1%.

In the wireless sensor network (WSN), nodes show a low forwarding rate under a poor-quality links environment and a resource-constrained state. The malicious nodes imitate this forwarding behavior, which can selectively forward date, eavesdropping, or discarding important dates. The traditional reputation model is challenging to identify with this kind of sub-attack nodes. To address these problems, a malicious node identification strategy based on time reputation model and environmental parameters optimization (TRM-EPO) is proposed in the WSN. First of all, the comprehensive reputation is calculated according to the direct reputation and the recommended indirect reputation. The environmental parameters matrix is based on nodes' running state, taking into account nodes' energy, data volume, number of adjacent nodes, and node sparsity. Besides, according to the environmental parameters matrix, and the recorded comprehensive reputation matrix, the next cycle's trust can be predicted. Finally, a similarity of the actual reputation and predicted trust matrix is proposed to compare with an adaptive threshold to identify malicious nodes. The experimental results demonstrate that the proposed strategy improves sensor nodes' security and reliability in a complex environment. Moreover, compared to comparison algorithms, the TRM-EPO improves the recognition rate above 1% and reduces the false-positive rate by more than 1%.

无线传感器网络在动态变化的信道和干扰环境工作时,为获得较高的信干噪比,节点会提高发射功率,致使节点间的干扰不断增大,为抵消其带来的消极影响,节点将继续增加发射功率,这将导致网络环境逐渐恶化,同时过多浪费节点能量.针对以上问题,本文提出一种合作博弈下无线传感器网络功率控制策略,为使节点能够更加精准的根据周围环境信息动态调节发射功率,算法引入节点间距离作为干扰权重因子以修正有效干扰模型,进而改进信干噪比模型;基于合作博弈理论将节点信息传输速率和自身剩余能量整合,建立合作博弈下的效用函数,在对不同效用权重因子下的归一化信息传输速率、发射功率方差值、信干噪比和网络效用4种结果进行综合权衡后,得出适当的效用权重因子值,并证明效用函数存在纳什均衡解,通过算法多次迭代后得出使网络效用达到最高时的节点最优发射功率.仿真结果表明,本文算法得出的最优发射功率方差小,算法收敛速度快,网络在节点较低发射功率时即可获得较高的信干噪比,网络生存周期得以延长,实现更高的网络效用.