Wireless Sensor Networks are generally employed for observing and monitoring specific environments. WSNs are made from a huge amount of low-cost sensor nodes separated and distributed in different environments for distributing data through sensor nodes. The collection of data by the various sensors were transmitted into the Base Station. An enhanced Trust-Based Adaptive Acknowledgment based Intrusion-Detection System was proposed from positive distributions in WSNs. A Kalman filter algorithm is used in Multi-objective Particle Swarm Optimization to predict trust nodes over the WSN. Simulations were carried out for non-malicious (0% malicious) networks, and various ranges of malicious nodes in the network were investigated. The outcomes show that the proposed MPSO achieves an improvement of 3.3% than PSO at 0% malicious nodes concerning the PDR. Similarly, at 30% malicious, the PDR of MPSO achieves better by 3.5% than PSO in WSN.

无线传感器网络通常用于观察和监视特定环境。WSN由大量低成本传感器节点组成，这些传感器节点在不同的环境中分离并分布，用于通过传感器节点分配数据。各种传感器收集的数据被传输到基站中。从无线传感器网络中的正分布出发，提出了一种基于信任的增强型基于自适应确认的入侵检测系统。多目标粒子群优化中使用卡尔曼滤波算法来预测WSN上的信任节点。对非恶意（0％恶意）网络进行了仿真，并调查了网络中各种范围的恶意节点。结果表明，在涉及PDR的恶意节点为0％的情况下，拟议的MPSO比PSO改进了3.3％。