An Evaluation of Machine Learning Algorithms To Detect Attacks in Scada Network

Today, with the digitization of manufacturing, industries are becoming more and more competitive and offer personalized products and services. With The Industry of the Future also known as Industry 4.0 or Fourth Industrial Revolution, architecture has fundamentally changed. It is based on intelligent automation and integrates a new technologies that allow permanent communication between machines and systems to ensure an increasing improvement in production. Despite the advantages of industry 4.0, there are also challenges to be addressed: Network security has become very important than before, therefore we have to guarantee better intrusion detection, based on machine learning algorithms. To this end, we examine four supervised learning methods, namely: Naïve Bayes, Support Vector Machines (SVM), Trees J48 and Random Forest, and compare the results using different performance measures (accuracy, recall, time to build the model etc). The data set used in this study comes from a laboratory scale gas pipeline, entitled “10% Random Sample Gas Pipeline The objective of this research is to select the best algorithm to detect and predict attacks in SCADA Network, in order to take preventive measures against the risk of intrusion.