Feature selection using hybrid GWO-PSO.

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.

将电力系统升级为智能电网（smart grids）带来了诸多益处，包括提升运行效率、增强系统可靠性以及推动可再生能源并网整合。但这一技术演进也使得电网面临更高的网络安全风险，尤其是虚假数据注入攻击（False Data Injection Attacks, FDIAs）。传统入侵检测系统（Intrusion Detection Systems, IDS）多依赖预定义规则与特征签名，难以识别复杂的FDIAs，存在明显的检测局限性。针对这一技术空白，本文提出了一种新型入侵检测系统，其融合混合特征选择与深度学习分类器技术，用于智能电网场景下的FDIAs检测，核心目标是提升智能电网中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算法（Particle Swarm Optimization, PSO）与灰狼优化算法（Grey Wolf Optimization, GWO）实现混合特征选择，确保筛选出与FDIAs检测最相关的特征子集。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与时序特征。本研究用于性能评估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FDIAs攻击场景。实验结果表明，所提入侵检测框架的性能显著优于传统方法：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；该混合深度学习分类器在准确率、召回率、精确率与F1值等关键指标上均表现更优。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；通过捕捉覆盖所有攻击类型的关键特征提升了召回率，同时通过减少假阳性样本降低了不必要的干预操作，优化了精确率；F1值兼顾了召回率与精确率，证明该检测系统具备优异的鲁棒性与可靠性。本研究提出了一种实用的双混合入侵检测框架，用于智能电网中的FDIAs检测，弥补了现有入侵检测技术的缺陷。未来研究可聚焦于采用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，以及解决大规模部署场景下的实时处理与可扩展性挑战。