Detecting and identifying cyber attacks is crucial to prevent cyber attacks such as advanced sustainable threats, promote the healthy development of network infrastructure, and guarantee the safe and stable operation of network facilities. In this paper, the key characteristics of network attacks in network traffic data are selected by using mutual information theory, a gray wolf boosting algorithm is proposed by improving the gray wolf optimization algorithm, and a GWB-LSSVM model is provided based on this algorithm and least squares support vector machine. The model shows good detection performance for the current main forms of network attacks. The experimental results based on NSL-KDD data set show that its detection precision, detection rate and detection accuracy reached 99.7%, 99.3% and 99.1% respectively. Compared with some existing research work, its detection precision is improved by up to about 2.58%, detection rate by up to about 3.98%, detection accuracy by up to about 3.78%, and the training time of the model by up to 55.9%.