Open Access
| Issue |
MATEC Web Conf.
Volume 292, 2019
23rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
|
|
|---|---|---|
| Article Number | 03017 | |
| Number of page(s) | 4 | |
| Section | Computers | |
| DOI | https://doi.org/10.1051/matecconf/201929203017 | |
| Published online | 24 September 2019 | |
- A. Ahmim, L. Maglaras, M. A. Ferrag, M. Derdour and H. Janicke, A Novel Hierarchical Intrusion Detection System based on Decision Tree and Rules-based Models. In Proceedings of SecRIoT 2019, 1st International Workshop on Security and Reliability of IoT Systems. Santorini Island, Greece, May 29-31, 2019. [Google Scholar]
- https://www.techopedia.com/definition/29738/zero-day-attack. [Google Scholar]
- R. Singh, H. Kumar, R. K. Singla, R. R. Ketti, Internet attacks and intrusion detection system: A review of the literatur, Online Information Review, 41, 2, pp. 171-184, (2017) https://doi.org/10.1108/OIR-12-2015-0394 Permanent link to this document: https://doi.org/10.1108/OIR-12-2015-0394. [CrossRef] [Google Scholar]
- S. Naseer, Y. Saleem, S. Khalid, M. K. Bashir, J. Han, M. M. Iqbal and K. Han, Enhanced Network Anomaly Detection Based on Deep Neural Networks. IEEE Access, Special section on cyber-threats and countermeasures in the healthcare sector. [Google Scholar]
- T. Auld, A. W. Moore and S. F. Gull, Bayesian Neural Networks for Internet Traffic Classification. IEEE Transactions on Neural Networks, 18, 1 (2007). [CrossRef] [Google Scholar]
- B. Shah and B. H. Trivedi, Artificial Neural Network based Intrusion Detection System: A Survey. International Journal of Computer Applications (0975 – 8887), 39, 6 (2012). [Google Scholar]
- N. el Kadhi, K. Hadjar and N. el Zant, A Mobile Agents and Artificial Neural Networks for Intrusion Detection. Journal of Software, 7, 1 (2012). [CrossRef] [Google Scholar]
- C. V. Zhou, C. Leckie and S. Karunasekera, A survey of coordinated attacks and collaborative intrusion detection. Computers and Security, 29, 1, pp.124-140 (2010). [CrossRef] [Google Scholar]
- A. Shenfield, D. Day and A. Ayesh, Intelligent intrusion detection systems using artificial neural networks. ICT Express 4, pp. 95–99 (2018). [CrossRef] [Google Scholar]
- M. Amruta and N. Talhar, Effective Denial of Service Attack Detection using Artificial Neural Network for Wired LAN. In Proceedings of SCOPES - International conference on Signal Processing, Communication, Power and Embedded System (2016). [Google Scholar]
- M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, A detailed analysis of the KDD CUP 99 data set, in Proc. IEEE Symp. Comput. Intell. Secur. Defense Appl. (CISDA). Piscataway, NJ, USA: IEEE Press, 2009, pp. 53–58. [Online]. Available: http://dl.acm.org/citation.cfm?id=1736481.1736489. [Google Scholar]
- S. D. Bay, D. Kibler, M. J. Pazzani, and P. Smyth, The UCI KDD archive of large data sets for data mining research and experimentation, ACM SIGKDD Explor. Newslett., 2, 2, pp. 81–85, 2000. [Google Scholar]
- Mathworks, Matlab neural network toolbox. https://uk.mathworks.com/products/neural-network.html, 2016. [Google Scholar]
- Canadian Institute for Cybersecurity, CICIDS2017 dataset.Available: https://www.unb.ca/cic/datasets/ids-2017.html. [Google Scholar]
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