Application of a Neocortex Model to Identify Contextual Anomalies in the Industrial Internet of Things Network Traffic

被引：0
|
作者：
Markov, G. A. [1 ]
机构：
[1] Jet Infosystems, Moscow 127015, Russia
来源：
AUTOMATIC CONTROL AND COMPUTER SCIENCES | 2023年 / 57卷 / 08期
关键词：
hierarchical-temporal memory; artificial intelligence; contextual anomalies; machine learning; neocortex; industrial Internet of Things; network traffic; HTM;
D O I：
10.3103/S0146411623080163
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper examines the problem of identifying network anomalies when processing data streams in industrial systems. A network anomaly refers to a malicious signature and the current context: network environment and topology, routing parameters, and node characteristics. As a result of the study, it is proposed to use a neocortex model that supports the memory mechanism to detect network anomalies.
引用
收藏
页码：1018 / 1024
页数：7
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