基于半监督学习的工业控制网络安全基线技术研究

Yixiang Jiang, Chengting Zhang, Wen Jin
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引用次数: 0

摘要

随着工业控制网络的快速发展,基于网络流量数据的性能管理和风险防范,特别是异常流量检测逐渐受到人们的重视。然而,传统的基于固定基线的流量检测方法已不能适应日益增长的数据量和日益复杂的数据类型。导致检测结果不准确和虚警,也消耗了大量的人力和资源。本文提出了一种半监督学习方法来实现基线的自构建和异常指标数据的自动检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Baseline Technology of Industrial Control Network Security based on Semi-supervised Learning
With the rapid development of industrial control network, performance management and risk prevention based on network traffic data, especially abnormal traffic detection, have gradually attracted people's attention. However, the traditional flow detection method based on fixed baseline cannot adapt to the growing data and increasingly complex data types. It leads to inaccurate test results and false alarms, and also consumes a lot of manpower and resources. In this paper, a semisupervised learning method is proposed to realize the self-construction of baseline and the automatic detection of abnormal index data.
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