Cyber security has to gain a high level of awareness in the Network and Computer pasture due to the large spread of information transmission technology. A powerful False Data Injection (FDI) Intruder monitors the network activities and injects the malicious data thereby causing failure in the power system. To overcome this defense, the “Conviction based Intruder Defense Model” is proposed to identify and isolate it from the network by providing secure transmission. This scheme operates in three phases. In the first phase, the data are analyzed with the library files to identify the conviction values. Based on the conviction values the resulting factors are analyzed with different iterations and the suspicious drafts are identified and classified using Fuzzy Intrusion Detection System (FIDS) divider. In the second phase, three algorithms are used to organize the drafts categorized. In the third phase, abnormal nodes are isolated from the network. Experimental results show higher accuracy and detection rates with low false positives.

由于信息传输技术的广泛传播，网络安全必须在网络和计算机牧场上获得高度的关注。强大的错误数据注入（FDI）入侵者可以监视网络活动并注入恶意数据，从而导致电源系统出现故障。为了克服这种防御，提出了“基于信念的入侵者防御模型”，以通过提供安全的传输将其识别并与网络隔离。该方案分三个阶段进行。在第一阶段，使用库文件分析数据以识别信念值。根据置信度值，使用不同的迭代对结果因子进行分析，并使用模糊入侵检测系统（FIDS）除法器对可疑草稿进行识别和分类。在第二阶段 三种算法用于组织分类的草稿。在第三阶段，异常节点与网络隔离。实验结果表明，较高的准确性和检测率且误报率较低。