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

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 c...

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Vydáno v:Applied sciences Ročník 9; číslo 2; s. 238
Hlavní autoři: Yang, Yanqing, Zheng, Kangfeng, Wu, Chunhua, Niu, Xinxin, Yang, Yixian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 10.01.2019
Témata:
Accuracy
Algorithms
Artificial intelligence
Classification
Clustering
Computer networks
Datasets
deep belief networks
Deep learning
fuzzy aggregation
intrusion detection
Intrusion detection systems
Machine learning
modified density peak clustering algorithm
Neural networks
Principal components analysis
restricted Boltzmann machine
Teaching methods
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app9020238