Open Access
MATEC Web Conf.
Volume 139, 2017
2017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
Article Number 00029
Number of page(s) 5
Published online 05 December 2017
  1. A.A. Aburomman, M.B.I. Reaz. A survey of intrusion detection systems based on ensemble and hybrid classifiers. Computers & Security, 65, (2017), 135–152. [CrossRef] [Google Scholar]
  2. M.H. Bhuyan, D.K. Bhattacharyya, J.K. Kalita. Network Anomaly Detection: Methods, Systems and Tools. IEEE Communications Surveys & Tutorials, (2013), 1–34. [Google Scholar]
  3. R. Sonawane, T. Tajane, P. Chavan et al. Anomaly based intrusion detection network system. Software Engineering and Technology, 8(3), (2016), 66–69. [Google Scholar]
  4. M.M. Breunig. H.P. Kriegel, R.T. Ng. LOF: identifying density-based local outliers. ACM, 29(2), (2000), 93–104. [Google Scholar]
  5. H.P. Kriegel, M.S. Hubert, A. Zimek. Angle-based outlier detection in high-dimensional data. Acm Sigkdd International Conference on Knowledge Discovery & Data Mining, (2008), 444–452. [Google Scholar]
  6. C.F. Tsai, K.C. Cheng. Simple instance selection for bankruptcy prediction. Knowledge-Based Systems, 27(3), (2012), 333–342. [CrossRef] [Google Scholar]
  7. T. Liang. Research on Intrusion Detection Technology Based on Clustering Analysis. Chongqing University, (2010). [Google Scholar]
  8. C. Guo. Research on the Key Technology of Network Intrusion Detection Based on Data Mining [Ph.D. Dissertation]. Beijing: Beijing University of Posts and Telecommunications, (2014). [Google Scholar]
  9. L. Xiao, Z. Shao, G. Liu. K-means Algorithm Based on Particle Swarm Optimization Algorithm for Anomaly Intrusion Detection. World Congress on Intelligent Control & Automation, 2, (2006), 5854–5858. [CrossRef] [Google Scholar]
  10. H.G. Kayacik, A.N. Zincir-Heywood, M.I. Heywood. A hierarchical SOM-based intrusion detection system. Engineering Applications of Artificial Intelligence, 20(4), (2007), 439–451. [CrossRef] [Google Scholar]
  11. L. Kuang, M. Zulkernine. An anomaly intrusion detection method using the CSI-KNN algorithm. Acm Symposium on Applied Computing, (2008), 921–926. [Google Scholar]
  12. U. Ravale, N. Marathe, P. Padiya. Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function. Procedia Computer Science, 45(39), (2015), 428–435. [CrossRef] [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.