Journal
2017 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP)
Volume -, Issue -, Pages 9-16Publisher
IEEE
DOI: 10.1109/smartcomp.2017.7946998
Keywords
DDoS attack; deep learning; recurrent neural network; LSTM
Categories
Funding
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1624782] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Office of Advanced Cyberinfrastructure (OAC) [1229576] Funding Source: National Science Foundation
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Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal threats to the Internet. Automatically detecting DDoS attack packets is one of the main defense mechanisms. Conventional solutions monitor network traffic and identify attack activities from legitimate network traffic based on statistical divergence. Machine learning is another method to improve identifying performance based on statistical features. However, conventional machine learning techniques are limited by the shallow representation models. In this paper, we propose a deep learning based DDoS attack detection approach (called DeepDefense). Deep learning approach can automatically extract high-level features from low-level ones and gain powerful representation and inference. We design a recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities. The experimental results demonstrate better performance of our model compared with conventional machine learning models. We reduce the error rate from 7.517% to 2.103% compared with conventional machine learning method in the larger data set.
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