DAD-MCNN: DDoS Attack Detection via Multi-channel CNN

With the continuous development of web services, the web security becomes more and more important. Distributed Denial of Service (DDoS) attack as one of the most common form of attacks, has produced serious economic damages. DDoS attack detection as one of main defense methods is suffered increasing attention by researchers. Most of them use machine learning methods to make good detection performance. However, there are still gaps between real detection rate and expected one, conventional machine learning methods are limited compared with deep learning. In this paper, we propose DAD-MCNN, a multi-channel CNN(MC-CNN) based DDoS attack detection framework, which can fully utilize information from a huge amount of network packages and set up an earlier warning system. Our contributions can be summarized as follows: (1) we propose a new preprocessing method for the network dataset. (2) MC-CNN is applied to detect DDoS attack and the detection result is decided by data in respective channels. (3) We use incremental training method to optimize training procedures and time in MC-CNN. (4) The experiment result shows that MC-CNN has the highest accuracy compared with conventional machine learning methods. The result also proves that our approach has performed well not only in DDoS attack detection but also in other anomaly attack detection.