Efficient Classification of Enciphered SCADA Network Traffic in Smart Factory Using Decision Tree Algorithm
Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae‐Min Lee, Dong‐Seong Kim
Abstract
Vulnerability detection in Supervisory Control and Data Acquisition (SCADA) network of a Smart Factory (SF) is a high-priority research area in the cyber-security domain. Choosing an efficient Machine Learning (ML) algorithm for intrusion detection is a huge challenge. This study performed an investigative analysis into the classification ability of various ML models leveraging public cyber-security datasets to determine the best model. Based on the performance evaluation, all adaptions of Decision Tree (DT) and KNN in terms of accuracy, training time, MCE, and prediction speed are the most suitable ML for resolving security issues in the SCADA system.
Topics & Concepts
SCADAComputer scienceDecision treeIntrusion detection systemVulnerability (computing)Factory (object-oriented programming)Data miningAlgorithmDomain (mathematical analysis)Random treeMachine learningTree (set theory)Network securityIndustrial control systemArtificial intelligenceReal-time computingComputer securityControl (management)EngineeringMathematicsMotion planningMathematical analysisElectrical engineeringProgramming languageRobotNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesSmart Grid Security and Resilience