Improving Data Protection in Industrial Control System Networks Using Machine Learning Technique

@article{1703291,
      author = {Jude Chinedu Akamadu, Prof. James Eke, Emetu Chukwuma Kalu},
      title = {Improving Data Protection in Industrial Control System Networks Using Machine Learning Technique},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {9},
      pages = {374-381},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703291.pdf},
      abstract = {This work was embarked on to combat the security vulnerabilities trending on the present-day Industrial Control Systems (ICS) Supervisory Control and Data Acquisition (SCADA) system infrastructure. This was observed after series of literatures were discussed and research gap was established. The study proposed to solve the problem using machine learning. Data of the ICS Denial of Service Attack was collected and then develop a neural network based algorithm with it to detect threat on ICS. The training was done using back propagation algorithm. The system was implemented using Mathlab and neural network toolbox. The model was simulated and the result showed good threat detection performance with regression value of 0.973 and detection response time of 12ms which is very good. The percentage improvement when compared with the characterized test bed is 13.3% which is very good.},
      keywords = {Data protection, control system networks, and machine learning},
      month = {March},
  }