Quarterly journal published in SPbPU
and edited by prof. Dmitry Zegzhda
Peter the Great St. Petersburg Polytechnic University
Institute of computer sciences and technologies
information security of computer systems
Information Security Problems. Computer Systems
Published since 1999.
ISSN 2071-8217
DETECTION OF COMPUTER ATTACKS IN NETWORKS OF INDUSTRIAL INTERNET OF THINGS BASED ON THE COMPUTING MODEL OF HIERARCHICAL TEMPORARY MEMORY
G. A. Markov, V. M. Krundyshev, M. O. Kalinin, D. P. Zegzhda, A. G. Busygin Jet Infosystems Company, Peter the Great St. Petersburg Polytechnic University (SPbPU)
Annotation: This paper discusses the problem of detecting network anomalies caused by computer attacks in industrial Internet of Things networks. To detect anomalies, a new method has been developed using the technology of hierarchical temporary memory, which is based on the innovative neocortex model. An experimental study of the developed anomaly detection method based on the HTM model demonstrated the superiority of the developed solution over the LSTM-based analogue. The developed prototype of the anomaly detection system provides continuous online unsupervised learning, takes into account the current network context, and also applies the accumulated experience by supporting the memory mechanism.
Keywords: hierarchical temporary memory, artificial intelligence, computer attacks, neocortex, online learning, sparse distributed representations, network traffic, HTM.
Pages 163-172