MFDI: Securing IIoT: An Investigation intoMachine Learning-Based False Data Injection Attacks

Now a days, Industries like manufacturing, power generation, and smart transportation are using advanced internet-connected systems. Unfortunately, this makes them more vulnerable to cyberattacks. To protect against these attacks, policymakers have created rules, implemented security features like secure login and encryption to safeguard these systems. However, the increasing number of cyberattacks like SQL injection attacks, False data Injection attacks, Phishing, etc. shows that these measures are not enough, and have some limitations. The false data injection attack is one of the most accruing attacks possible on Physical and cyber layers both. In Ukraine, there is a total blackout due to false data injection attacks, such case study of FDI attacks in power systems shows the severity of false data injection attacks. In this FDI attack, machines show wrong readings which might lead to economic and life loss, as critical infrastructures like hospitals, smart grids, modern transportation systems, etc. are fully dependent on electricity. In this paper, a few best-performing Machine learning algorithms have been implemented and after Exploratory Data Analysis and Data Preprocessing steps, GridSearchCv is used for hyperparameter tuning, which makes our proposed model outperformer than previous works in FDI attack detection. To evaluate the proposed model’s reliability, the performance evaluation is also done on Power System Attack Datasets provided by Mississippi State University and Oak Ridge National Laboratory. The Decision Tree algorithm stands out as the top performer with a training accuracy of 100% and a testing accuracy of 98%. the other performance metrics like Recall, F1 score, sensitivity training, and testing time also scored well in terms of comparison with other algorithms.