The technological evolution of the smart grids is going to take its shape in the form of a new paradigm called the Internet of Energy (IoE); which is considered to be the convergence of internet, communication, and energy. Like other evolved technologies, the IoE inherits security vulnerabilities from its constituents that need to be addressed. Intrusion Detection Systems (IDS) have been used to counteract malicious attacks. Among the types of IDS, anomaly-based IDS that employ mostly machine learning algorithms are considered to be the promising one, owing to their capability of detecting zero-day attacks. However, using complex algorithms to detect attacks, the existing anomaly-based IDS designed for IoE require considerable amount of time. It is tempting to reduce the training and testing time in order to make the IDS feasible for the IoE architecture. In this paper, we propose a hybrid anomaly-based IDS that can be installed at any networked site of the IoE architecture, such as Advanced Metering Infrastructure (AMI), to counteract security attacks. Our proposed system reduces the overall classification time of detection compared to the existing hybrid methods. The proposed solution uses a combination of K-means and Support Vector Machine, where the K-means centroids are used in a unique training method that reduces the training and testing times of the Support Vector Machine without compromising classification performance. We choose the best value of “k” and fine-tuned the SVM for best anomaly detection. Our approach achieves the highest accuracy of 99.9% in comparison with the existing approaches.

智能电网的技术发展将以一种称为能源互联网（IoE）的新范例的形式来形成。被认为是互联网，通讯和能源的融合。像其他演进的技术一样，IoE从需要解决的组成部分继承了安全漏洞。入侵检测系统（IDS）已被用来抵抗恶意攻击。在IDS的类型中，由于其具有检测零日攻击的能力，因此大多采用机器学习算法的基于异常的IDS被认为是很有前途的。但是，使用复杂的算法检测攻击时，为IoE​​设计的现有基于异常的IDS需要大量时间。试图减少培训和测试时间以使IDS在IoE架构中可行是很诱人的。在本文中，我们提出了一种基于混合异常的IDS，该IDS可以安装在IoE体系结构的任何联网站点上，例如Advanced Metering Infrastructure（AMI），以应对安全攻击。与现有的混合方法相比，我们提出的系统减少了总的检测分类时间。提出的解决方案结合了K均值和支持向量机，其中K均值质心以一种独特的训练方法使用，可减少支持向量机的训练和测试时间，而不会影响分类性能。我们选择“ k”的最佳值，并对SVM进行微调以实现最佳的异常检测。我们的方法可达到99的最高准确度。