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Lim, Sunghoon
Unstructured Data Mining and Machine Learning Lab
Research Interests
  • Unstructured Data Mining, Machine Learning, Industrial Artificial Intelligence (AI+X)

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A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

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Title
A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection
Author
Tama, Bayu AdhiLim, Sunghoon
Issue Date
2021-02
Publisher
Tech Science Press
Citation
Computers, Materials and Continua, v.66, no.2, pp.2217 - 2227
Abstract
An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS, where deep learning (e.g., deep neural network [DNN]) is used as base learner model. The effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics, i.e., Matthew’s correlation coefficient, accuracy, and false alarm rate. The results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.
URI
https://scholarworks.unist.ac.kr/handle/201301/48818
URL
https://www.techscience.com/cmc/v66n2/40636
DOI
10.32604/cmc.2020.012432
ISSN
1546-2218
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SME_Journal Papers
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