A Performance Analysis Approach for Network Intrusion Detection Algorithms
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-72792-5_20
Reference25 articles.
1. Xie, J., Li, S., Zhang, Y., et al.: A method based on hierarchical spatiotemporal features for trojan traffic detection. In: 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), pp. 1–8 (2019)
2. Li, Z., Batta, P., Trajkovic, L.: Comparison of machine learning algorithms for detection of network intrusions. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4248–4253 (2018)
3. Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mob. Netw. Appl. (2019). https://doi.org/10.1007/s11036-019-01424-2, online available
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5. Ahmad, I., Basheri, M., Iqbal, M.J., et al.: Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6, 33789–33795 (2018)
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