Identification of Power Information Network System Vulnerabilities by using Cascading Failure Analysis and Differential Evaluation with Maturity Factor

In recent days, the power systems becoming a vital component in the modern society, which is playing a critical role in sustaining daily life. However, the vulnerabilities of these power systems are exposed through unforeseen accidents or intentional attacks and the compromising reliability. In existing, with the widespread application of data mining technology, the vulnerabilities are identified with various optimizations such as Genetic Algorithm (GA), which are mainly suffered with low convergence. To overcome this issue, Cascading Failure Analysis and Differential Evaluation with Maturity Factor (CFA-DE-MF) proposed for vulnerability identification. Initially, the Cascading Failure Analysis (CFA) is introduced to find the potential failures that are present in the power information network security. Next, the convergence sets are removed if any overload branches exist. After that, the criticality/ importance are ranked with the help of two techniques to rank the failures into high, medium or low. The considered techniques are Dynamic Network Stability (DNS) and Branch Overload Frequency (BOF), which rank the failures based on DNS metrics and frequency overload. Finally, the vulnerabilities are identified with the help of Differential Evaluation with Maturity Factor (DE-MF) optimization. From the results, the proposed CFA-DE-MF established better results such as Area Under Curve (AUC) (99.1%), accuracy (97.5%) and F1-score (75.9%) respectively.