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, thedata 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.