In the technology age, the use of networks has hugely increased. this led to an increment in the number of attackers. A network attack is an try to achieve unauthorized access to personnel of an organization’s network, steal data or perform other malicious activity. Machine Learning is a subset of artificial Intelligence techniques that teaches machines to learn from historical information. In this paper, a machine learning-based approach was developed to detect network attacks. Two Machine learning models were used: Support vector machine and Artificial neural network. In this approach, a feature selection step based on the p-value is executed first to reduce the size of the dataset. After that, training and testing steps were performed. The proposed approach was tested on a real dataset collected from Kaggle. Confusion matrix, recall, precision, and f1 score were used to test the performance of the used ML techniques. The result shows the efficiency of this approach.