MATEC Web of Conferences
Volume 159, 2018The 2nd International Joint Conference on Advanced Engineering and Technology (IJCAET 2017) and International Symposium on Advanced Mechanical and Power Engineering (ISAMPE 2017)
|Number of page(s)||6|
|Published online||30 March 2018|
An Integration of PSO-based Feature Selection and Random Forest for Anomaly Detection in IoT Network
Information Security and Internet Applications Lab., IT Convergence and Application Engineering, Pukyong National University Daeyon Campus, 45, Yongso-ro, Nam-Gu. Busan, South Korea 48513
* Corresponding author: email@example.com
The most challenging research topic in the field of intrusion detection system (IDS) is anomaly detection. It is able to repeal any peculiar activities in the network by contrasting them with normal patterns. This paper proposes an efficient random forest (RF) model with particle swarm optimization (PSO)-based feature selection for IDS. The performance model is evaluated on a well-known benchmarking dataset, i.e. NSL-KDD in terms of accuracy, precision, recall, and false alarm rate(FAR) metrics. Furthermore, we evaluate the significance differencesbetween the proposed model and other classifiers, i.e. rotation forest (RoF)and deep neural network (DNN) using statistical significance test. Basedon the statistical tests, the proposed model significantly outperforms otherclassifiers involved in the experiment.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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