Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach

Abstract

The paper presents an approach to detection of attacks against Internet-of-Things networks and devices which can be used in critical infrastructures. It is based on use of machine learning and big data processing. Feature of the offered approach is using the method of reduction of output data sets and application of various algorithms of machine learning based on distributed data processing. The paper compares the speed and accuracy of attack detection on the basis of machine learning algorithms in the local and distributed modes.