Abstract
The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.
Highlights
With the passage of time, the smart grid has made great progress in both technical and practical levels, and the ensuing smart grid security issues have attracted more and more attention
Featuring its prominent optimization ability about classification performance, the genetic algorithm was proposed as a solution to raise model accuracy based on the fast training and detecting speed already secured by the over-learning machine
By mapping the input weight in the hidden layer bias map to chromosome vectors and utilizing the test errors as fitness functions in the genetic algorithm, this GA-based algorithm was able to optimize the parameters based on genetic traits, enhancing the performance of an over-learning model
Summary
With the passage of time, the smart grid has made great progress in both technical and practical levels, and the ensuing smart grid security issues have attracted more and more attention. In 2009, the smart meter system of a US power grid company was attacked by hackers, resulting in enormous economic losses [1]. In 2015, a power system in Ukraine was targeted by a Denial of Service (DoS) network attack, resulting in a large-scale blackout in the region [3]. It is obvious that smart grids bring convenience, as well as new challenges, to society; targeted research must be carried out. The United States, Europe and many other countries, as well as China, have successively carried out some researches in smart grid security and explored the field of smart grid intrusion detection
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