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Open AccessArticle

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

1
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
2
School of Mathematical and Computer Science, Ningxia Normal University, Guyuan 756000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Muhammad Imran, Athanasios V. Vasilakos, Thaier Hayajneh and Neal N. Xiong
Sensors 2016, 16(10), 1701; https://doi.org/10.3390/s16101701
Received: 26 July 2016 / Revised: 8 October 2016 / Accepted: 8 October 2016 / Published: 13 October 2016
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. View Full-Text
Keywords: intrusion detection system; deep neural network; ensemble model; wireless sensor network; spectral clustering intrusion detection system; deep neural network; ensemble model; wireless sensor network; spectral clustering
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Ma, T.; Wang, F.; Cheng, J.; Yu, Y.; Chen, X. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks. Sensors 2016, 16, 1701.

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