An Adam-Optimised Deep Neural Net for Predicting Vulnerability Attack in Software Defined Networking

Authors

  • Umar Abdullahi Umar Department of Mathematics and Computer Science, Faculty of Natural and Applied Sciences, Sule Lamido University 048 SLU Kafin Hausa, Jigawa, Nigeria.
  • Babangida Isyaku Department of Mathematics and Computer Science, Faculty of Natural and Applied Sciences, Sule Lamido University 048 SLU Kafin Hausa, Jigawa, Nigeria.
  • Isa Modibbo Ismail Department of Mathematics and Computer Science, Faculty of Natural and Applied Sciences, Sule Lamido University 048 SLU Kafin Hausa, Jigawa, Nigeria.

DOI:

https://doi.org/10.56471/slujst.v2i1.80

Abstract

The potentiality of Software Defined networking (SDN) made network management easier through the centralize controller. Therefore, SDN is a rule-based network instead of destination based used in the legacy router. The behavior of the entire network strongly depends on the forwarding rules installed in the switch logical data structure called flowtable. Arrival of traffic flows forces switch to trigger packet-in event to request for the installation of correspondent flows. Traffic flows arrive in the switch more frequent, in this way the number of events also augment. Unfortunately, the switch memory resource is constraint which cannot accommodate the number of active networks which in turn increases the chances of flowtable overflow.Therefore,anattacker leverage on the overflow to lunch several types of attack which include DoS attack thus, cause SDN Controller security concern.Consequently, avert writing forwarding rules from to legitimate clients. This call for predictive model to detect and verify rules before installation. This work proposed a predictive model that builds an-end to end machine learning based predictive model for detecting SDN Controller vulnerability. NSL KDD Dataset was used as training and validation dataset to preprocessand build the model. A number ofbenchmarks NN parameters were used to measure and tune the performance of the proposed model. A prediction accuracy of about 97% accuracy was obtained on the validation set. The individual identification of each attack benefits in applying the mitigation technique to prevent the specific type of traffic flows responsible for the attack, this analysis is based on a single source of real network traffic.

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Published

2021-01-23