Deep learning

Inference

Computer network

TRACE (psycholinguistics)

Application layer DDoS attack

The Internet

World Wide Web

Machine learning

Network packet

Philosophy

Artificial intelligence

Botnet

Set (abstract data type)

Programming language

Denial-of-service attack

Computer science

Linguistics

Trinoo

Artificial neural network

Feature learning

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 (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 a 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.

DeepDefense: Identifying DDoS Attack via Deep Learning