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Wireless Communications and Mobile Computing
Volume 2018, Article ID 7472095, 10 pages
https://doi.org/10.1155/2018/7472095
Research Article

Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
2Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA

Correspondence should be addressed to Fuhong Lin; nc.ude.btsu@nilhf

Received 5 September 2017; Accepted 16 November 2017; Published 14 January 2018

Academic Editor: Shangguang Wang

Copyright © 2018 Xingshuo An et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value.