Open Access
Issue |
ITM Web Conf.
Volume 21, 2018
Computing in Science and Technology (CST 2018)
|
|
---|---|---|
Article Number | 00027 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/itmconf/20182100027 | |
Published online | 12 October 2018 |
- Panda Security, PandaLabs’ Annual Report 2017 (2017) [Google Scholar]
- S. Morgan, Cybersecurity Ventures, 2017 Cybercrime Report (2017) [Google Scholar]
- Republic of Poland Governmental Computer Security Incident Response Team CERT.GOV.PL, Report on the security status of the cyberspace of the Republic of Poland in 2017 (2018) [Google Scholar]
- Republic of Poland Governmental Computer Security Incident Response Team CERT.GOV.PL, Report on the security status of the cyberspace of the Republic of Poland in 2016 (2017) [Google Scholar]
- Republic of Poland Governmental Computer Security Incident Response Team CERT.GOV.PL, Report on the security status of the cyberspace of the Republic of Poland in 2015 (2016) [Google Scholar]
- Republic of Poland Governmental Computer Security Incident Response Team CERT.GOV.PL, Report on the security status of the cyberspace of the Republic of Poland in 2014 (2015) [Google Scholar]
- Republic of Poland Governmental Computer Security Incident Response Team CERT.GOV.PL, Report on the security status of the cyberspace of the Republic of Poland in 2013 (2014) [Google Scholar]
- C. Beek, T. Dunton et al., McAfee, McAfee Labs Threats Report June 2018 (2018) [Google Scholar]
- A. Szmit, M. Szmit, On the use of econometric models for forecasting network traffic., Scientific Notebooks Organization and Management of the Lodz University of Technology 55, 1154, p.193–201 (2013) [Google Scholar]
- J. Rusnacko, Self-optimizing traffic classification framework, Faculty of Informatics Masaryk University (2013) [Google Scholar]
- D. M. S. Zekrifa, Hybrid Intrusion Detection System. Theses, School of Information Technology & Mathematical Sciences (2014) [Google Scholar]
- N.F. Haq, M. Rafni, A.R. Onik et al., Application of Machine Learning Approaches in Intrusion Detection System: A Survey. International Journal of Advanced Research in Artificial Intelligence, 4, 3, p. 9–18 (2015) [Google Scholar]
- KDD Cup 1999, Available on: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html [Google Scholar]
- S. Mukkamala, G. Janoski, A.H. Sung, Intrusion Detection Using Neural Networks and Support Vector Machines. IEEE International Joint Conference on Neural Networks 2002, IEEE Computer Society Press, p. 1702–1707 (2002) [Google Scholar]
- J. Hussain, S. Lalmuanawma, L. Chhakchhuak, A two-stage hybrid classification technique for network intrusion detection system, International Journal of Computational Intelligence Systems, 9, 5, p. 863–875 (2016) [CrossRef] [Google Scholar]
- Chen, W. H., S. H. Hsu, H. P. Shen, Application of SVM and ANN for intrusion detection, Computers & Operations Research, 32, 10, p. 2617–2634 (2005) [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.