Intrusion detection model for software-defined networking based on feature selection

Abstract

Due to the problems of feature redundancy and high dimensionality in Software-Defined Networking (SDN) intrusion detection, the original Seagull Optimization Algorithm (SOA) appears to have lack of exploration capability and tends to fall into local optimum problems. A hybrid strategy Improved Seagull Optimization Algorithm (ISOA) is proposed to solve these problems. Firstly, a cubic chaotic mapping strategy is used to initialize the population, laying the foundation for the global search for an optimum. Secondly, a Cauchy-Gauss variation strategy is introduced to improve the search efficiency of the algorithm. Finally, a Simulated Annealing algorithm is combined with a certain probability to accept a solution that is worse than the current one, jumping out of the local optimum. The ISOA was applied to the InSDN dataset to verify the intrusion detection performance of the model. Comparing with the original SOA and six other classical algorithm models, the data dimensionality was significantly reduced and effectively improving the performance of SDN intrusion detection.