Open Access
| Issue |
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
|
|
|---|---|---|
| Article Number | 03027 | |
| Number of page(s) | 6 | |
| Section | Parallel Session II: Water System Technology | |
| DOI | https://doi.org/10.1051/matecconf/201824603027 | |
| Published online | 07 December 2018 | |
- Tan, Zhiyuan, et al. Enhancing Big Data Security with Collaborative Intrusion Detection. IEEE Cloud Computing, (2015), 1 (3): 27-33. [CrossRef] [Google Scholar]
- Mylavarapu, Goutam, J. Thomas, and T. K. Ashwin Kumar. Real-Time Hybrid Intrusion Detection System Using Apache Storm. IEEE, International Conference on High PERFORMANCE Computing and Communications IEEE, (2015): 1436-1441. [Google Scholar]
- Kietz, J U, et al. “Semantics Inside!” But let’s not tell the Data Miners: Intelligent Support for Data Mining. The Semantic Web: Trends and Challenges. Springer International Publishing, (2014): 706-720. [Google Scholar]
- Kulariya, Manish, et al. Performance analysis of network intrusion detection schemes using Apache Spark. International Conference on Communication and Signal Processing IEEE, (2016): 1973-1977. [Google Scholar]
- Neethu, B. Classification of Intrusion Detection Dataset using machine learning Approaches. International Journal of Electronics & Computer Science Engineering, (2012), 1 (3): 1044-1051. [Google Scholar]
- Chauhan, Himadri, et al. A Comparative Study of Classification Techniques for Intrusion Detection. International Symposium on Computational and Business Intelligence IEEE Computer Society, (2013): 40-43. [Google Scholar]
- Hua, Hui You, et al. Hybrid Kmeans with KNN for Network Intrusion Detection Algorithm. Computer Science, (2016). [Google Scholar]
- Si, Haiyang, et al. The Performance Evaluation of Intrusion Detection Evaluation Method Based on Bayesian Theory. International Conference on Wireless Communications, NETWORKING and Mobile Computing IEEE, (2008): 1-4. [Google Scholar]
- Yao, Wei, J. Wang, and S. Zhang. Intrusion detection model based on decision tree and Naive-Bayes classification. Journal of Computer Applications, (2015), 7 (12): 2883-2885. [Google Scholar]
- Quan, Liang Liang, and W. U. Wei-Dong. Anomaly detection model based on support vector machine and Bayesian classification. Journal of Computer Applications, (2012), 32 (6): 1632-1635. [CrossRef] [Google Scholar]
- Tavallaee M, Bagheri E, Lu W, et al. A detailed analysis of the KDD CUP 99 data set. IEEE International Conference on Computational Intelligence for Security and Defense Applications. IEEE Press, (2009): 53-58. [Google Scholar]
- Paulauskas N, Auskalnis J. Analysis of data preprocessing influence on intrusion detection using NSL-KDD dataset. Electrical, Electronic and Information Sciences. IEEE, (2017): 1-5. [Google Scholar]
- Alhomoud A, Munir R, Disso J P, et al. Performance Evaluation Study of Intrusion Detection Systems. Procedia Computer Science, (2011), 5 (9): 173-180. [CrossRef] [Google Scholar]
- Panigrahi A, Patra M R. Performance Evaluation of Rule Learning Classifiers in Anomaly Based Intrusion Detection. Computational Intelligence in Data Mining-Volume 2. Springer India, (2016). [Google Scholar]
- Belavagi M C, Muniyal B. Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. Procedia Computer Science, (2016), 89 :117-123. [CrossRef] [Google Scholar]
- Patel A, Qassim Q, Wills C. A survey of intrusion detection and prevention systems. Journal of Network & Computer Applications, (2015), 36 (1): 25-41. [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.