EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 232 (2018) MATEC Web Conf., 232 (2018) 01011 References
Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 01011
Number of page(s) 5
Section Network Security System, Neural Network and Data Information
DOI https://doi.org/10.1051/matecconf/201823201011
Published online 19 November 2018
  1. E. Bulut, B.K. Szymanski. Exploiting Friendship Relations for Efficient Routing in Mobile Social Networks[J]. IEEE Transactions on parallel and distributed systems. 2012, 23(12): 2254-2265 [CrossRef] [Google Scholar]
  2. A. Khosravi and J. Pan. Exploring personal interest in intermittently connected wireless mobile social networks[A]. In IEEE Consumer Communications and Networking Conference (CCNC2011) [C], 2011, pp503-508 [Google Scholar]
  3. N. Kayastha, W. Ping, et al. Applications, Architectures, and Protocol Design issues for Mobile Social Networks: A Survey[J]. Proceedings of the IEEE. 2011, 99(12): 2130-2158 [CrossRef] [Google Scholar]
  4. YANG Fengrui, ZHENG Yunjun, ZHANG Chang. Hybrid recommendation algorithm based on probability matrix factorization. Journal of Computer Applications, 2018, 38(3): 644-649. [Google Scholar]
  5. SHEN Xueli, QIN Shujuan. Anomaly detection based on synthetic minority oversampling technique and deep belief network. Journal of Computer Applications, 2018, 38(7): 1941-1945. [Google Scholar]
  6. YANG Y H, HUANG H Z, SHEN Q N, et al. Research on intrusion detection based on incremental GHSOM[J]. Chinese Journal of Computers, 2014, 37(5): 1216-1224. [Google Scholar]
  7. CHEN H, WAN G X, XIAO Z J. Intrusion detection method of deep belief network model based on optimization of data processing[J]. Journal of Computer Applications, 2017, 37(6): 1636-1643. [Google Scholar]
  8. YIN C L, ZHU Y F, FEI J L, et al. aA deep learning approach for intrusion detection using recurrent neural networks[J]. IEEE Access, 2017, 5:21954-21961. [CrossRef] [Google Scholar]
  9. GAO N, HE Y Y, GAO L. Deep learning method for intrusion detection in massive data[J]. Applications Research of Computers, 2018, 35(4): 1197-1200. [Google Scholar]
  10. ZHOU Yuhao, ZHANG Hongling, LI Fangfei, QI Peng. Local focus support vector machine algorithm. Journal of Computer Applications, 2018, 38(4): 945-948. [Google Scholar]
  11. ZHANG Y, LI Z R, LIU X D. Active learning SMOTE based imbalanced data classification[J]. Computer Applications and Software, 2012, 29(3): 91-93. [Google Scholar]
  12. CAO J X, DONG D, XU S, et al. A k-core based algorithm for influence maximization in social networks[J]. Chinese Journal of Computers, 2015, 38(2): 238-248. [Google Scholar]
  13. WANG C, CHEN W, WANG Y. Scalable influence maximization for independent cascade model in large-scale social networks[J]. Data Mining & Knowledge Discovery, 2012, 25(3): 545-576 [CrossRef] [Google Scholar]
  14. NING Xueli, LUO Yonglong, XING Kai, ZHENG Xiaoyao. Frequent location privacy-preserving algorithm based on geosocial network. Journal of Computer Applications, 2018, 38(3): 688-692. [Google Scholar]
  15. YE A Y, LI Y C,MA J F, et al. Location privacypreserving method of k-anonymous based-on service similarity[J]. Journal on Communications, 2014, 35(11): 162-169. [Google Scholar]
  16. WU Zheng, YU Hongtao, LIU Shuxin, ZHU Yuhang. User identification across multiple social networks based on information entropy. Journal of Computer Applications, 2017, 37(8): 2374-2380. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.