With the accelerated development of computer networks utilization and the enormous growth of the number of applications running on top of it, network security becomes more significant. Intrusion Detection Systems (IDS) is considered as one of the essential tools utilized to protect computer networks and information systems. Software-defined network (SDN) architecture is used to provide network monitoring and analysis mechanism due to the programming environment of the SDN controller. On the other hand intrusion detection system is developed to monitor incoming traffic to the SDN network; hence it enables SDN to adjust security service insertion. In this paper, an efficient intrusion detection system using CNN is proposed and applied on a new attack-specific SDN dataset called InSDN. The proposed model is outperformed in compared with different machine learning algorithms such as CART, LR, LDA, SVM, NB and AB.