PSO parameters.

Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures. This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. The main objective is to enhance the accuracy and robustness of IDS in smart grids. The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data’s spatial and temporal features. The dataset used for evaluation, the Industrial Control System (ICS) Cyber Attack Dataset (Power System Dataset) consists of various FDIA scenarios simulated in a smart grid environment. Experimental results demonstrate that the proposed IDS framework significantly outperforms traditional methods. The hybrid feature selection effectively reduces the dimensionality of the dataset, improving computational efficiency and detection performance. The hybrid deep learning classifier performs better in key metrics, including accuracy, recall, precision, and F-measure. Precisely, the proposed approach attains higher accuracy by accurately identifying true positives and minimizing false negatives, ensuring the reliable operation of smart grids. Recall is enhanced by capturing critical features relevant to all attack types, while precision is improved by reducing false positives, leading to fewer unnecessary interventions. The F-measure balances recall and precision, indicating a robust and reliable detection system. This study presents a practical dual-hybrid IDS framework for detecting FDIAs in smart grids, addressing the limitations of existing IDS techniques. Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.

将电力系统现代化升级为智能电网（smart grids）已带来诸多益处，包括提升运行效率、增强系统可靠性以及推动可再生能源的整合接入。然而，这一技术进步也显著提升了电网面临网络威胁的脆弱性，尤以虚假数据注入攻击（False Data Injection Attacks, FDIAs）为甚。传统入侵检测系统（Intrusion Detection Systems, IDS）往往难以识别复杂的虚假数据注入攻击，因其依赖预定义规则与特征签名。本研究针对这一研究空白，提出了一种新型入侵检测系统，该系统融合混合特征选择与深度学习分类器，以检测智能电网环境中的虚假数据注入攻击。本研究的核心目标是提升智能电网场景下入侵检测系统的检测精度与鲁棒性。所提方法结合粒子群优化（Particle Swarm Optimization, PSO）与灰狼优化（Grey Wolf Optimization, GWO）实现混合特征选择，确保选取与虚假数据注入攻击检测最相关的核心特征。此外，该入侵检测系统采用融合卷积神经网络（Convolutional Neural Networks, CNN）与长短期记忆网络（Long Short-Term Memory, LSTM）的混合深度学习分类器，以捕捉智能电网数据的空间特征与时序特征。本研究评估所用的数据集为工业控制系统（Industrial Control System, ICS）网络攻击数据集（电力系统数据集），该数据集包含在智能电网环境中模拟的多种虚假数据注入攻击场景。实验结果表明，所提出的入侵检测系统框架显著优于传统检测方法。混合特征选择可有效降低数据集维度，提升计算效率与检测性能。混合深度学习分类器在精度、召回率、精确率与F1测度（F-measure）等关键指标上表现更优。具体而言，所提方法通过精准识别真阳性样本并最小化假阴性样本，实现了更高的检测精度，保障了智能电网的可靠运行。召回率的提升得益于对所有攻击类型相关关键特征的有效捕捉，而精确率的优化则通过减少假阳性样本实现，从而降低不必要的系统干预操作。F1测度兼顾了召回率与精确率，表明该检测系统具备出色的鲁棒性与可靠性。本研究提出了一种实用的双混合入侵检测系统框架，用于检测智能电网中的虚假数据注入攻击，弥补了现有入侵检测技术的局限。未来研究可聚焦于引入真实智能电网数据开展验证、开发自适应学习机制、探索其他仿生优化算法，以及应对大规模部署中的实时处理与可扩展性挑战。