An Intrusion Detection System Using Feature Selection Based on Red Kangaroo Mating Algorithm
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-8086-2_36
Reference12 articles.
1. Bajaj K, Arora A (2013) Improving the intrusion detection using discriminative machine learning approach and improve the time complexity by data mining feature selection methods. Int J Comput Appl 76(1):5–11. https://doi.org/10.5120/13209-0587
2. de la Hoz E, Ortiz A, Ortega J, de la Hoz E (2013) Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques. In: International conference on hybrid artificial intelligence systems. Springer, pp 103–111.https://doi.org/10.1007/978-3-642-40846-5_11
3. Kang SH, Kim KJ (2016) A feature selection approach to find optimal feature subsets for the network intrusion detection system. Cluster Comput 19:325–333. https://doi.org/10.1007/s10586-015-0527-8
4. Osanaiye O, Cai H, Choo KKR, Dehghantanha A, Xu Z, Dlodlo M (2016) Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP J Wirel Commun Netw 130.https://doi.org/10.1186/s13638-016-0623-3
5. Bamakan SMH, Wang H, Yingjie T, Shi Y (2016) An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing 199:90–102. https://doi.org/10.1016/j.neucom.2016.03.031
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