基于机器学习算法的电力信息网络安全态势感知

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2023-11-27 DOI:10.1080/09540091.2023.2284649

Chao Wang, Jia-han Dong, Guang-xin Guo, Tian-yu Ren, Xiao-hu Wang, Ming-yu Pan

引用次数: 0

摘要

为了正确预测这些网络的安全状态，我们提出了一种基于机器学习算法的电力信息网络安全状态检测方法。一个感知模型…

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Security situational awareness of power information networks based on machine learning algorithms

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.