Hybrid DDoS Detection Framework Using Matching Pursuit Algorithm
Derya Erhan, Emin Anarım
Abstract
Although a considerable amount of research has been done on DDoS attacks, it still poses a severe threat to many businesses and internet service providers. DDoS attacks commonly generate a high amount of network traffic. However, the resource depletion DDoS attacks can deny the target service, although it generates much less traffic than legitimate traffic. We propose a novel DDoS detection framework using the Matching Pursuit algorithm to detect resource depletion type DDoS attacks. We use multiple characteristics of network traffic simultaneously in order to detect low-density DDoS attacks efficiently. The proposed method uses the dictionary produced from the parameters of the network traffic using the K-SVD algorithm.Dictionary generation using network traffic, provides legitimate and attack traffic models, and adds adaptability of the proposed method to network traffic. We also implement DDoS detection approaches that use Matching Pursuit and Wavelet techniques and compare them using two different data sets. Additionally, we offer a hybrid DDoS detection framework that combines these approaches with a decision-making mechanism using an artificial neural network. We evaluate the proposed methods with two different data sets. The proposed approaches perform over 99% true positive rate with a false positive rate lower than 0.7% with a low-density DDoS attack dataset. In the hybrid intrusion detection system with more than one attack, the detection performances of other methods have decreased, while the proposed approach achieves true positive rates higher than 99% with a false positive rate lower than 0.7%.