用混合模型提升电网入侵检测准确率至99.7%
A Hybrid CNN-LSTM Deep Learning Model for Intrusion Detection in Smart Grid
- 结合CNN特征提取与LSTM时序建模，捕捉网络异常
- 在DNP3和IEC104数据集上达到99.7%检测准确率
- 适合电力安全、工业控制系统防护研究者参考
传统电网向智能电网演进，融合可再生能源与现代通信技术，提升了能源管理效率，但也增加了被攻击的风险，可能导致隐私泄露、运行中断甚至大规模停电。基于SCADA的智能电网协议虽支持实时数据采集与控制，却易受未授权访问和拒绝服务（DoS）等攻击。本文提出一种基于深度学习的混合入侵检测系统（IDS），利用卷积神经网络（CNN）的特征提取能力与长短期记忆网络（LSTM）的时序模式识别优势，以DNP3和IEC104入侵检测数据集进行训练与测试，有效识别并分类潜在网络威胁。实验结果表明，相比其他深度学习方法，该模型在准确率、精确率、召回率和F1分数上均有显著提升，检测准确率达到99.70%。
原文摘要 · Abstract (English)
The evolution of the traditional power grid into the "smart grid" has resulted in a fundamental shift in energy management, which allows the integration of renewable energy sources with modern communication technology. However, this interconnection has increased smart grids' vulnerability to attackers, which might result in privacy breaches, operational interruptions, and massive outages. The SCADA-based smart grid protocols are critical for real-time data collection and control, but they are vulnerable to attacks like unauthorized access and denial of service (DoS). This research proposes a hybrid deep learning-based Intrusion Detection System (IDS) intended to improve the cybersecurity of smart grids. The suggested model takes advantage of Convolutional Neural Networks' (CNN) feature extraction capabilities as well as Long Short-Term Memory (LSTM) networks' temporal pattern recognition skills. DNP3 and IEC104 intrusion detection datasets are employed to train and test our CNN-LSTM model to recognize and classify the potential cyber threats. Compared to other deep learning approaches, the results demonstrate considerable improvements in accuracy, precision, recall, and F1-score, with a detection accuracy of 99.70%.
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