摘 要：工业控制系统（ICS）作为关键基础设施的核心组成部分，其网络安全问题日益凸显，传统信息安全技术难以有效应对ICS网络中的特殊威胁。为此，本文旨在研究适用于工业控制环境的威胁检测与防御策略，以提升系统的安全性和稳定性。研究通过分析ICS协议特性及常见攻击手段，提出了一种基于深度学习的异常检测模型，该模型结合时间序列特征提取与行为模式识别，能够实时监测网络流量并发现潜在威胁。同时，设计了多层次防御框架，集成入侵检测、访问控制和应急响应机制，以降低攻击影响并增强系统韧性。实验结果表明，所提方法在检测精度和误报率方面均优于现有方案，尤其对隐蔽性较强的APT攻击具有显著优势。本文的主要贡献在于将深度学习技术与ICS安全需求深度融合，为构建智能化、主动化的工业网络安全防护体系提供了新思路。
关键词：工业控制系统；深度学习；异常检测
Abstract：Industrial Control Systems (ICS), as a core component of critical infrastructure, have increasingly highlighted cybersecurity concerns, with traditional information security technologies struggling to effectively address the unique threats within ICS networks. To this end, this study focuses on investigating threat detection and defense strategies tailored for industrial control environments to enhance system security and stability. By analyzing the characteristics of ICS protocols and common attack methodologies, a deep learning-based anomaly detection model is proposed, which integrates time-series feature extraction with behavioral pattern recognition to enable real-time network traffic monitoring and identification of potential threats. Additionally, a multi-layered defense fr amework is designed, incorporating intrusion detection, access control, and incident response mechanisms to mitigate attack impacts and strengthen system resilience. Experimental results demonstrate that the proposed method outperforms existing solutions in terms of detection accuracy and false positive rate, particularly exhibiting significant advantages in detecting advanced persistent threats (APTs) with high stealth characteristics. The primary contribution of this work lies in the deep integration of deep learning techniques with the specific security requirements of ICS, providing novel insights for constructing intelligent and proactive industrial cybersecurity protection systems.
引言 1
一、工业控制系统网络安全概述 1
(一)工业控制系统网络架构分析 1
(二)网络安全威胁的主要来源 2
(三)安全防护的基本原则 2
二、威胁检测技术与方法 3
(一)威胁检测的核心技术框架 3
(二)异常行为监测与分析方法 3
(三)数据驱动的威胁检测模型 4
三、防御策略设计与实施 4
(一)防御体系架构的设计原则 4
(二)关键节点的安全加固措施 5
(三)实时响应与应急处置机制 5
四、案例分析与效果评估 6
(一)典型工业场景的应用实践 6
(二)威胁检测与防御的效果评价 6
(三)改进方向与未来展望 7
结论 7
参考文献 8
致谢 8
关键词：工业控制系统；深度学习；异常检测
Abstract：Industrial Control Systems (ICS), as a core component of critical infrastructure, have increasingly highlighted cybersecurity concerns, with traditional information security technologies struggling to effectively address the unique threats within ICS networks. To this end, this study focuses on investigating threat detection and defense strategies tailored for industrial control environments to enhance system security and stability. By analyzing the characteristics of ICS protocols and common attack methodologies, a deep learning-based anomaly detection model is proposed, which integrates time-series feature extraction with behavioral pattern recognition to enable real-time network traffic monitoring and identification of potential threats. Additionally, a multi-layered defense fr amework is designed, incorporating intrusion detection, access control, and incident response mechanisms to mitigate attack impacts and strengthen system resilience. Experimental results demonstrate that the proposed method outperforms existing solutions in terms of detection accuracy and false positive rate, particularly exhibiting significant advantages in detecting advanced persistent threats (APTs) with high stealth characteristics. The primary contribution of this work lies in the deep integration of deep learning techniques with the specific security requirements of ICS, providing novel insights for constructing intelligent and proactive industrial cybersecurity protection systems.
Keywords: Industrial Control System；Deep Learning；Anomaly Detection
引言 1
一、工业控制系统网络安全概述 1
(一)工业控制系统网络架构分析 1
(二)网络安全威胁的主要来源 2
(三)安全防护的基本原则 2
二、威胁检测技术与方法 3
(一)威胁检测的核心技术框架 3
(二)异常行为监测与分析方法 3
(三)数据驱动的威胁检测模型 4
三、防御策略设计与实施 4
(一)防御体系架构的设计原则 4
(二)关键节点的安全加固措施 5
(三)实时响应与应急处置机制 5
四、案例分析与效果评估 6
(一)典型工业场景的应用实践 6
(二)威胁检测与防御的效果评价 6
(三)改进方向与未来展望 7
结论 7
参考文献 8
致谢 8
