Research on Machine Learning-Based Advanced Semantic Mapping Model for Substations and Security Alert Disposition

One of the critical challenges in establishing a robust connection between substations, security systems, and operators lies in generating an advanced semantic mapping model. The paper treats this task as a binary category issue. Every pair of substation variable and security element serves as a sample; two classifications, "Matched" and "Unmatched," indicate the relation between these two in a particular sample. Establishing a suitable semantic mapping parallels categorizing the sample into a "Matched" class using a potent machine learning method and a well-trained model. Two varieties of sample characteristics are chosen. Syntactic elements denote the sample's syntactical structure, while semantic features illustrate various bonds between the substation variable and the security element in each sample. Machine learning algorithms aid in understanding cyber phenomena and abstraction of the underlying phenomena into a model for better prediction of future values, thereby detecting anomalies. The study employs open-source machine learning algorithms, such as Weka, Orange, and RapidMiner. Initial experiment results suggest that the cumulative precision of the learning-based model surpasses that of other standard methods, thus offering a promising approach for substations and security alert disposition.