Wireless communication has changed and improved people's lives and society, especially with the arrival of the Internet of Things (IoT) era. Despite the maturity of wireless communication, the security issue of communication remains the most stubborn and troublesome problem due to the increasingly complex and large amounts of data. An intrusion detection system is the guarantee of secure communication. However, variable protocols and drastic growth in data volume make intrusion detection a difficult task. In this paper, we proposed a framework of anomaly-based network intrusion detection system to finish the detection job. First, UNSW-NB15 is selected as the research object. Based on this new dataset, we built a detection model combining a deep learning method and a shallow learning approach. The former one is a deep auto-encoder used for feature learning, which can discover important representations of data and accelerate detection. The latter one is a powerful support vector machine (SVM), where the artificial bee colony (ABC) algorithm is used to find optimal parameters for SVM with five-fold cross validation (5FCV). Various experiments are conducted and the simulation results prove that the proposed method performs quite better than some of state-of-the-art intrusion detection approaches, including the method based on the principal component analysis (PCA) and some other machine learning strategies.