| Popis: |
Software Defined Networking (SDN) is a new network architecture in which network control is separated from direct traffic and is programmed directly. Any change in network information and its configuration can be easily implemented in software by using the controller. Although SDN networks with their new structure and controller make way for new and innovative applications for network administrators, but the security challenges and attacks of SDN networks have created problems for these networks. One of these malicious attacks is Distributed Denial of Service (DDoS) attacks. The DDoS attack is aimed at removing machine and network resources from its legitimate users. In this paper, we propose a hybrid method for detecting DDoS attacks in SDN Networks. This method is consisting of statistical and machine learning method. Statistical method calculates the new correlation measure among all features and the dynamic thresholds, then extracts a portion of the data is recognized as attack. This portion is then redirected to the machine learning section to increase the DDoS detection accuracy. The experimental results on UNB-ISCX, CTU-13 and ISOT datasets showed that the proposed method outperforms the existing techniques in terms of the accuracy of detecting DDOS attacks in SDN networks. |
