In order to accurately detect the false data injection attack for node voltage in the cyber-physical power system, an attack detection method based on recurrent neural network (RNN) is proposed in this paper. The feat...
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In order to accurately detect the false data injection attack for node voltage in the cyber-physical power system, an attack detection method based on recurrent neural network (RNN) is proposed in this paper. The features of voltage data of power topology nodes and the construction method of attack vector are studied. Based on the fast regression algorithm, the best strategy for the data integrity attack of the specific node voltage is solved. The RNN is used to reconstruct time series of the node voltage, and set the threshold of error between the input data and the output data. Then, by calculating whether the error between the reconstructed output data and the original input data exceeds the threshold, it is determined whether the system has suffered data integrity attack. Finally, the feasibility and effectiveness of the detection method proposed in this paper were verified by simulated attack experiments.
This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of...
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This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L-2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches. (C) 2014 Elsevier B.V. All rights reserved.
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