期刊
IEEE ACCESS
卷 6, 期 -, 页码 59657-59671出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2875045
关键词
Abnormal behavior detection; big data; deep belief networks; deep learning; ensemble classifier
资金
- Natural Science Foundation of China [61502118, 61872104]
- Natural Science Foundation of Heilongjiang Province in China [F2016028]
- Fundamental Research Fund for the Central Universities in China [HEUCF180602, HEUCFM180604]
- National Science and Technology Major Project [2016ZX03001023-005]
- Natural Science Foundation of Daqing Normal University [17ZR04]
The emergence of Internet connectivity has led to a significant increase in the volume and complexity of cyber attacks. Abnormal behavior detection systems are valuable tools for ensuring the security in computer networks. However, due to the huge amount and ever increasing diversity of the intrusions, the existing intrusion detection systems, which use machine learning techniques to learn a classifier based on a handcrafted feature vector, are not robust enough to detect sophisticated attacks which cause a high false alarm rate. Therefore, building a flexible in-depth defense system to detect abnormal behavior requires an ability to automatically learn powerful features and analyze large amounts of network traffic. To address these concerns, this paper proposes a novel distributed approach for the detection of abnormal behavior in large-scale networks. The developed model discovers the abnormal behavior from large-scale network traffic data using a combination of a deep feature extraction and multi-layer ensemble support vector machines (SVMs) in a distributed way. First, we perform a non-linear dimensionality reduction, achieved through a distributed deep belief networks on large-scale network traffic data. Then, the obtained features are fed to the multi-layer ensemble SVM. The construction of the ensemble is accomplished through the iterative reduce paradigm based on Spark. Empirical results show a promising gain in performance compared with other existing models.
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