In wireless sensor networks, or WSNs, a malicious node is able to cover itself by switching between good and bad behaviors. Even when running under a reputation mechanism, such a node can still behave maliciously now and then so long as its reputation is within the acceptable level. To address this inconsistent behavior issue, a combined approach of statistic reputation and time series is proposed in this study, in which the negative binomial reputation is applied to rate the nodes' reputation and concept of time series is borrowed to analyze the reputation results. Simulations show that the proposed method can effectively counter inconsistent behavior nodes and thus improves the overall system performance.

In wireless sensor networks, the on-off attacker nodes can present good behaviors and then opportunistically and selectively behave badly to compromise the network. Such misbehaving nodes are usually difficult to be spotted by the network system in a short term. To address this issue, in this study, we propose a reputation scheme to mitigate the on-off attack. In addition, a penalty module is properly designed so that the reputation scheme can effectively respond to the on-off misbehaviors and make such nodes quickly detected by the system, hence the minimization of their influence. We confirm the feasibility and effectiveness of the proposed scheme through simulation tests.

In this study, we propose a statistical reputation approach for constructing a reliable packet route in ad-hoc sensor networks. The proposed method uses reputation as a measurement for router node selection through which a reliable data route is constructed for packet delivery. To refine the reputation, a transaction density is defined here to showcase the influence of node transaction frequency over the reputation. And to balance the energy consumption and avoid choosing repetitively the same node with high reputation, node remaining energy is also considered as a reputation factor in the selection process. Further, a shortest-path-tree routing protocol is designed so that data packets can reach the base station through the minimum intermediate nodes. Simulation tests illustrate the improvements in the packet delivery ratio and the energy utilization.

The unattended malicious nodes pose great security threats to the integrity of the IoT sensor networks. However, preventions such as cryptography and authentication are difficult to be deployed in resource constrained IoT sensor nodes with low processing capabilities and short power supply. To tackle these malicious sensor nodes, in this study, the trust computing method is applied into the IoT sensor networks as a light weight security mechanism, and based on the theory of Chebyshev Polynomials for the approximation of time series, the trust data sequence generated by each sensor node is linearized and treated as a time series for malicious node detection. The proposed method is evaluated against existing schemes using several simulations and the results demonstrate that our method can better deal with malicious nodes resulting in higher correct packet delivery rate.

Misbehaving nodes intrinsic to the physical vulnerabilities of ad-hoc sensor networks pose a challenging constraint on the designing of data fusion. To address this issue, a statistics-based reputation method for reliable data fusion is proposed in this study. Different from traditional reputation methods that only compute the general reputation of a node, the proposed method modeled by negative binomial reputation consists of two separated reputation metrics: fusion reputation and sensing reputation. Fusion reputation aims to select data fusion points and sensing reputation is used to weigh the data reported by sensor nodes to the fusion point. So, this method can prevent a compromised node from covering its misbehavior in the process of sensing or fusion by behaving well in the fusion or sensing. To tackle the unexpected facts such as packet loss, a discounting factor is introduced into the proposed method. Additionally, Local Outlier Factor (LOF) based outlier detection is applied to evaluate the behavior result of sensor nodes. Simulations show that the proposed method can enhance the reliability of data fusion and is more accurate than the general reputation method when applied in reputation evaluation.