Applying machine learning and parallel data processing for attack detection in IoT
Alexander Branitskiy, Igor Kotenko, Igor Saenko
Abstract
Internet of Things (IoT) networks are kind of computer networks for which the problem of information security and, in particular, computer attack detection is acute. For solving this task the paper proposes a joint application of methods of machine learning and parallel data processing. The structure of basic classifiers is determined, which are designed for detecting the attacks in IoT networks, and a new approach to their combining is proposed. The statement of classification problem is formed in which the integral indicator of effectiveness is the ratio of accuracy to time of training and testing. For enhancing the speed of training and testing we propose the usage of the distributed data processing system Spark and multi-threaded mode. Moreover, a dataset pre-processing procedure is suggested, which leads to a significant reduction of the training sample volume. An experimental assessment of the proposed approach shows that the attack detection accuracy in IoT networks approaches 100 percent, and the speed of dataset processing increases in proportion to the number of parallel threads.