Visible to the public Biblio

Filters: Author is Yan, X.  [Clear All Filters]
2019-09-09
Zhou, X., Lu, Y., Wang, Y., Yan, X..  2018.  Overview on Moving Target Network Defense. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). :821–827.
Moving Target Defense (MTD) is a research hotspot in the field of network security. Moving Target Network Defense (MTND) is the implementation of MTD at network level. Numerous related works have been proposed in the field of MTND. In this paper, we focus on the scope and area of MTND, systematically present the recent representative progress from four aspects, including IP address and port mutation, route mutation, fingerprint mutation and multiple mutation, and put forward the future development directions. Several new perspectives and elucidations on MTND are rendered.
2019-09-23
Tan, L., Liu, K., Yan, X., Wan, S., Chen, J., Chang, C..  2018.  Visual Secret Sharing Scheme for Color QR Code. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). :961–965.

In this paper, we propose a novel visual secret sharing (VSS) scheme for color QR code (VSSCQR) with (n, n) threshold based on high capacity, admirable visual effects and popularity of color QR code. By splitting and encoding a secret image into QR codes and then fusing QR codes to generate color QR code shares, the scheme can share the secret among a certain number of participants. However, less than n participants cannot reveal any information about the secret. The embedding amount and position of the secret image bits generated by VSS are in the range of the error correction ability of the QR code. Each color share is readable, which can be decoded and thus may not come into notice. On one hand, the secret image can be reconstructed by first decomposing three QR codes from each color QR code share and then stacking the corresponding QR codes based on only human visual system without computational devices. On the other hand, by decomposing three QR codes from each color QR code share and then XORing the three QR codes respectively, we can reconstruct the secret image losslessly. The experiment results display the effect of our scheme.

2021-03-30
Li, Y., Ji, X., Li, C., Xu, X., Yan, W., Yan, X., Chen, Y., Xu, W..  2020.  Cross-domain Anomaly Detection for Power Industrial Control System. 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC). :383—386.

In recent years, artificial intelligence has been widely used in the field of network security, which has significantly improved the effect of network security analysis and detection. However, because the power industrial control system is faced with the problem of shortage of attack data, the direct deployment of the network intrusion detection system based on artificial intelligence is faced with the problems of lack of data, low precision, and high false alarm rate. To solve this problem, we propose an anomaly traffic detection method based on cross-domain knowledge transferring. By using the TrAdaBoost algorithm, we achieve a lower error rate than using LSTM alone.