标题： 基于神经网络的智能电网家庭能源系统中FDI攻击的检测与多类分类 显示英文标题

标题： Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems

摘要： 虚假数据注入攻击（FDIAs）对智能电网基础设施，特别是家庭局域网络（HANs）构成重大威胁，因为在HANs中实时监控和控制得到了广泛应用。由于HANs的安全控制相对宽松且广泛可用，攻击者将其视为一个有吸引力的切入点，以操纵聚合需求模式，这最终会传播并破坏更广泛的电网操作。这些攻击破坏了智能电表数据的完整性，使恶意行为者能够在不触发传统警报的情况下操纵消耗值，从而在住宅和公用事业规模的基础设施中造成严重的漏洞。本文提出了一种基于机器学习的框架，使用住宅能源数据来检测和分类FDIAs。轻量级人工神经网络（ANN）提供实时检测，通过使用能源消耗、成本和时间上下文的关键特征进行工作。为了对不同类型的攻击进行分类，训练了一个双向LSTM来通过学习数据中的序列依赖性来识别正常、梯形和S型攻击形状。生成了一个合成的时间序列数据集来模拟现实的家庭行为。实验结果表明，所提出的模型在识别和分类FDIAs方面是有效的，为在边缘增强电网弹性提供了可扩展的解决方案。这项工作有助于构建智能、数据驱动的防御机制，从住宅端点加强智能电网网络安全。 显示英文摘要

摘要： False Data Injection Attacks (FDIAs) pose a significant threat to smart grid infrastructures, particularly Home Area Networks (HANs), where real-time monitoring and control are highly adopted. Owing to the comparatively less stringent security controls and widespread availability of HANs, attackers view them as an attractive entry point to manipulate aggregated demand patterns, which can ultimately propagate and disrupt broader grid operations. These attacks undermine the integrity of smart meter data, enabling malicious actors to manipulate consumption values without activating conventional alarms, thereby creating serious vulnerabilities across both residential and utility-scale infrastructures. This paper presents a machine learning-based framework for both the detection and classification of FDIAs using residential energy data. A real-time detection is provided by the lightweight Artificial Neural Network (ANN), which works by using the most vital features of energy consumption, cost, and time context. For the classification of different attack types, a Bidirectional LSTM is trained to recognize normal, trapezoidal, and sigmoid attack shapes through learning sequential dependencies in the data. A synthetic time-series dataset was generated to emulate realistic household behaviour. Experimental results demonstrate that the proposed models are effective in identifying and classifying FDIAs, offering a scalable solution for enhancing grid resilience at the edge. This work contributes toward building intelligent, data-driven defence mechanisms that strengthen smart grid cybersecurity from residential endpoints.