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Appl. Sci. 2019, 9(2), 238; https://doi.org/10.3390/app9020238

Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks

1
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
3
Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Received: 11 December 2018 / Revised: 1 January 2019 / Accepted: 4 January 2019 / Published: 10 January 2019
(This article belongs to the Section Computing and Artificial Intelligence)
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Abstract

Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision and F1-score than other well-known classification methods. Furthermore, the proposed model achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods. View Full-Text
Keywords: intrusion detection; modified density peak clustering algorithm; restricted Boltzmann machine; deep belief networks; fuzzy aggregation intrusion detection; modified density peak clustering algorithm; restricted Boltzmann machine; deep belief networks; fuzzy aggregation
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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 (CC BY 4.0).
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Yang, Y.; Zheng, K.; Wu, C.; Niu, X.; Yang, Y. Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks. Appl. Sci. 2019, 9, 238.

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