Sharaf Sanaa A, Ragab Mahmoud, Albogami Nasser, Al-Malaise Al-Ghamdi Abdullah, Sabir Maha Farouk, Maghrabi Louai A, Ashary Ehab Bahaudien, Alaidaros Hashem
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Sci Rep. 2024 Oct 4;14(1):23069. doi: 10.1038/s41598-024-74733-6.
A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain's integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.
智能电网（SG）是一种前沿的电网，它利用数字通信技术和自动化来有效处理电力消耗、分配和发电。它集成了储能系统、智能电表和可再生能源，以实现双向通信并增强电网模块之间的能量流动。由于存在网络攻击漏洞，智能电网需要强大的安全措施来保护敏感数据、确保公共安全并维持可靠的电力供应。包括入侵检测系统（IDS）在内的强大安全措施对于防范电网运营中的恶意操纵、未经授权的访问和数据泄露至关重要，从而确保电力供应链的完整性、恢复力和可靠性。深度学习（DL）通过有效分析网络数据、识别复杂攻击模式并实时适应动态威胁，提高了智能电网中的入侵识别能力，从而增强了电网抵御网络攻击的可靠性和恢复力。本研究在智能电网环境下开发了一种基于深度集成学习的新型山地瞪羚优化入侵检测（MGODEL-ID）技术。MGODEL-ID方法利用集成学习和元启发式方法来识别智能电网环境中的入侵。首先，MGODEL-ID方法利用Z分数归一化将输入数据转换为统一格式。此外，MGODEL-ID方法采用MGO模型进行特征子集选择。同时，入侵检测由长短期记忆（LSTM）、深度自动编码器（DAE）和极限学习机（ELM）这三个分类器的集成来执行。最终，利用蜣螂优化器（DBO）对分类器的超参数进行调整。进行了广泛的仿真结果以证明MGODEL-ID模型的改进安全结果。实验值表明，MGODEL-ID模型的性能优于其他模型。
