应用新皮质模型识别工业物联网网络流量中的上下文异常情况

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS

AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-02-29 DOI:10.3103/S0146411623080163

G. A. Markov

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引用次数: 0

摘要

摘要本文探讨了在处理工业系统数据流时识别网络异常的问题。网络异常指的是恶意签名和当前环境：网络环境和拓扑、路由参数和节点特征。研究结果建议使用支持记忆机制的新皮质模型来检测网络异常。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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

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.

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来源期刊

AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.70

自引率

22.20%

发文量

期刊介绍： Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision