AI-Integrated Feature Selection of Intrusion Detection for Both SDN and Traditional Network Architectures Using an Improved Crayfish Optimization Algorithm

With the birth of Software-Defined Networking (SDN), integration of both SDN and traditional architectures becomes the development trend of computer networks. Network intrusion detection faces challenges in dealing with complex attacks in SDN environments, thus to address the network security issues from the viewpoint of Artificial Intelligence (AI), this paper introduces the Crayfish Optimization Algorithm (COA) to the field of intrusion detection for both SDN and traditional network architectures, and based on the characteristics of the original COA, an Improved Crayfish Optimization Algorithm (ICOA) is proposed by integrating strategies of elite reverse learning, Levy flight, crowding factor and parameter modification. The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures, to reduce the dimensionality of the data and improve the performance of network intrusion detection. Finally, the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks. Experimental results show that ICOA improves the accuracy by 0.532% and 2.928% respectively compared with GWO and COA in traditional networks. In SDN networks, the accuracy of ICOA is 0.25% and 0.3% higher than COA and PSO. These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.