Safeguarding Station Data Integrity: A Comprehensive Study On Detecting And Mitigating False Data Injection Through Advanced Machine Learning Techniques

Abstract

 This article digs thoroughly into the core problem of avoiding fraudulent data injection and assuring station data integrity. The rising quantity of fraudulent data injection in station data creates major challenges for data dependability and system performance, requiring the deployment of improved detection algorithms. This work employs complex machine learning approaches, such as support vector machines (SVM), in an effort to adequately manage this difficulty. After obtaining information and producing features, we utilize three distinct SVM kernels (linear, rbf, and poly) to train the model. Accuracy, precision, and confusion matrix analysis are used to assess the model's performance. vital results indicate how successfully SVM classifiers recognize examples of altered data, offering new and vital information on how to enhance data integrity in station monitoring systems. This study establishes the framework for future breakthroughs in the area of data security by proving the efficiency of machine learning-driven techniques in tackling data security concerns.

Keywords— False Data Injection; Machine Learning; Station Data; Detection; SVM; Classification; Anomaly Detection; Data Integrity; Support Vector Machines (SVM); Supervised Learning; Feature Engineering; Confusion Matrix Analysis; Data Preprocessing; Cybersecurity; Data Quality Assurance; Malicious Attacks; Intrusion Detection; Data Security; Model Evaluation; Classification Algorithms; Performance Metrics; Data Validation; Cyber Threats; Data Anomalies; Machine Learning Models; Data Verification.