@inproceedings{416a58c16c1846b4b92c526b9a5f51a3,
title = "A Cascade-structured Meta-Specialists Approach for Neural Network-based Intrusion Detection",
abstract = "An ensemble learning approach for classification in intrusion detection is proposed. Its application to the KDD Cup 99 and NSL-KDD datasets consistently increases the classification accuracy compared to previous techniques. The cascade-structured meta-specialists architecture is based on a three-step optimization method: data augmentation, hyperparameters optimization and ensemble learning. Classifiers are first created with a strong specialization in each specific class. These specialists are then combined to form meta-specialists, more accurate than the best classifiers that compose them. Finally, meta-specialists are arranged in a cascading architecture where each classifier is successively given the opportunity to recognize its own class. This method is particularly useful for datasets where training and test sets differ greatly, as in this case. The cascade-structured meta-specialists approach achieved a very high classification accuracy (94.44\% on KDD Cup 99 test set and 88.39\% on NSL-KDD test set) with a low false positive rate (0.33\% and 1.94\% respectively).",
keywords = "Data augmentation, Ensemble learning, Intrusion detection, KDD Cup 99, NSL-KDD, Neural networks",
author = "Maxime Labonne and Alexis Olivereau and Baptiste Polve and Djamal Zeghlache",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019 ; Conference date: 11-01-2019 Through 14-01-2019",
year = "2019",
month = feb,
day = "25",
doi = "10.1109/CCNC.2019.8651856",
language = "English",
series = "2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019",
}