微型电脑应用

以往的虚假数据注入攻击防御方法仅计算攻击模型的单一参数，导致防御效果较差。为此，设计基于KNN分类算法的电力系统网络虚假数据注入攻击防御方法。根据攻击数据的特性，设计虚假数据注入攻击的理论模型与数学模型，并计算攻击模型的复杂程度和性能参数。在KNN分类算法的支持下，计算不同防御节点之间的距离，并对节点置信度进行描述，再通过信息身份验证，从而确定防御节点的位置。计算节点的数据传输函数和趋势函数，分析不同的攻击类型，从而采用不同防御策略。实验测试结果表明，与传统方法相比，应用该方法后，攻击入侵成功率与数据损失率均较低，说明该方法的在实际应用效果较好。

Previous methods for defending against false data injection attacks only calculate a single parameter of the attack model, resulting in poor defense effectiveness. To this end, a defense method for false data injection attacks in power system networks based on KNN classification algorithm is designed. A theoretical and mathematical model is designed for false data injection attacks based on the characteristics of the attack data, and the complexity and performance parameters of the attack model are calculated. With the support of KNN classification algorithm, the distance between different defense nodes is calculated, and the confidence level of the nodes is described. Through information authentication, the position of the defense nodes is determined. The data transfer function and trend function of nodes are calculatd, different types of attacks are analyzed, and different defense strategies are adopted. The experimental test results show that compared with traditional methods, the success rate of attack and intrusion and data loss rate are lower after applying this method, indicating that this method has better practical application effect.