Recognition and detection technology for abnormal flow of rebound type remote control Trojan in power monitoring system

Feng Sai, Xixuan Wang, Xiangtao Yu, Peipei Yan, Wei Ma
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引用次数: 1

Abstract

Energy security is related to national security, and power security is the core of energy security. With the process of intelligent transformation of power, the production network gradually moves from being closed to interconnection. Power production and operation are highly dependent on the power monitoring system and dispatching data network. Once an external attack breaks through The safety protection system will directly threaten the safe and stable operation of the power system, so higher requirements are put forward for the detection of abnormal flow in the power system. This paper designs an intrusion detection algorithm based on the normal flow threshold model based on the deep machine learning algorithm, and conducts a comparison test through the flow characteristic value, and finally verifies the accuracy and reliability of the abnormal flow detection algorithm proposed in this paper for modern power networks in different test environments.
电力监控系统中回弹式远程控制木马异常流量识别与检测技术
能源安全关乎国家安全,电力安全是能源安全的核心。随着电力智能化改造的进程,生产网络逐渐从封闭走向互联。电力生产运行高度依赖于电力监控系统和调度数据网络。一旦外部攻击突破安全保护系统,将直接威胁到电力系统的安全稳定运行,因此对电力系统异常流量的检测提出了更高的要求。本文设计了一种基于深度机器学习算法的正常流量阈值模型的入侵检测算法,并通过流量特征值进行对比测试,最终验证了本文提出的异常流量检测算法在不同测试环境下对现代电网的准确性和可靠性。
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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