Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks.