The Research of AMI Intrusion Detection Method using ELM in Smart Grid

Abstract

Advanced Metering Infrastructure(AMI) is a critical core component in smart grid. Currently, smart grid employs the same computer networks, which is vulnerable to suffer from cyber attacks. Furthermore, applications of traditional intrusion detection methods are restricted due to the limited computing capacity and potential deployment costs of electrical equipments. This paper proposes an ELM-based intrusion detection method for AMI. We first filter and partition the malicious data, and then different types of invasion are effectively extracted. Finally, we can use Extreme Learning Machine(ELM) for detecting different attack types of malicious data. However, traditional machine learning algorithms such as Support Vector Machine(SVM), which results in a longer training time and poor performance, and moreover SVM is not applicable to multi-class problems. In theory, ELM can approximate any target continuous function and classify any disjoint rejoins. As verified by the simulation results, ELM tends to have better scalability and achieve much better generalization performance at much faster learning speed than traditional SVM.