Intrusion Detection in Software Defined Networking using Genetic Algorithm

Software Defined Networking, or commonly called as SDN, is a fairly novel concept which makes use of the current conventional networking trends to provide the users a much flexible and customizable networking service. This networking concept puts forth the capability of centrally managing the network. One of the most prevalent concerns regarding the development of a Software Defined Networking System is network attacks. Network attacks may be easy to identify at times, but not always. The main task of an intrusion detection system is to detect and identify various threats and further action may include administration of prevention and mitigation strategies. Though this area of software defined networking is a widely researched topic, nowadays it is hard to detect most of the network attacks. A user may specify certain rules and actions to be taken in the case of a given set of network attacks, but it becomes impossible for man to predict the formation of new attacks. Thus, writing rules manually becomes a tedious task. The main objective of this paper is to attempt to automate rule generation for detection, prevention, and mitigation of the effects caused by every possible network attack. An attempt is being made to associate intrusion detection systems with Genetic Algorithm in order to predict the possibility of formation of new attacks, and to automate the rule generation for such predictive cases.