Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks
Khundrakpam Johnson Singh 1
Department of Computer Science and Engineering, National Institute of Technology, Durgapur 713209, India
2
Department of Computer Science and Engineering, National Institute of Technology, Manipur 795001, India
*
Author to whom correspondence should be addressed.
Academic Editor: Adom Giffin
Entropy 2016, 18(10), 350; https://doi.org/10.3390/e18100350
Received: 2 August 2016 / Revised: 15 September 2016 / Accepted: 19 September 2016 / Published: 1 October 2016
Distributed denial-of-service (DDoS) attack is one of the major threats to the web server. The rapid increase of DDoS attacks on the Internet has clearly pointed out the limitations in current intrusion detection systems or intrusion prevention systems (IDS/IPS), mostly caused by application-layer DDoS attacks. Within this context, the objective of the paper is to detect a DDoS attack using a multilayer perceptron (MLP) classification algorithm with genetic algorithm (GA) as learning algorithm. In this work, we analyzed the standard EPA-HTTP (environmental protection agency-hypertext transfer protocol) dataset and selected the parameters that will be used as input to the classifier model for differentiating the attack from normal profile. The parameters selected are the HTTP GET request count, entropy, and variance for every connection. The proposed model can provide a better accuracy of 98.31%, sensitivity of 0.9962, and specificity of 0.0561 when compared to other traditional classification models.
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Keywords:
DDoS attack; entropy; GA; MLP; variance
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Johnson Singh, K.; Thongam, K.; De, T. Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks. Entropy 2016, 18, 350.
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