SDN DDOS ATTACK IMAGE DATASET

It is now widely known fact that the Cloud computing and Software defined network paradigms have received a wide acceptance from researchers, academia and the industry. But the wider acceptance of cloud computing and SDN paradigms are hampered by increasing security threats. One of the several facts is that the advancements in processing facilities currently available are implicitly helping the attackers to attack in various directions. For example, it is visible that the conventional DoS attacks are now extended to cloud environments as DDoS attacks. With a huge number of security threats that are continuously occurring in computer networks and environments such as software defined networks (SDN) and Cloud computing, there is a demand to address security solutions that have a better reliability when compared to existing security solutions that are designed by considering datasets that did not meet the assessment and evaluation criterion which must be considered during the design of IDS systems. In [2], Nisha Ahuja, Gaurav Singal, and Debajyoti Mukhopadhyay have generated DDoS attack dataset for Software Defined Networks. This dataset was generated using mininet emulator. The dataset is available in the form of csv file(.csv) . The original version of dataset consists of 104345 traffic instances defined over 23 features.

For evaluating performance of ML and DL based Intrusion Detection System, we have converted the DDOS attack SDN Dataset [3]  in csv format to SDN DDoS attack image dataset consisting of network traffic image instances. Each traffic image instance in SDN DDoS attack image dataset is of 5x5 pixel size.