In the modern digital age, the importance of developing robust and effective measures to protect against cyberthreats cannot be overstated. The remarkable strides made in artificial intelligence (AI), particularly in areas such as computer vision and natural language processing, have demonstrated the potential of AI in enhancing cybersecurity measures. In this paper, we propose a novel machine learning method for detecting cybersecurity attacks based on ensemble approach. We combine several existing machine learning algorithms into a single meta-classifier to parse normal network flows from malicious signals. Concretely, five machine learning algorithms—random forest, XGBoost, support vector machines, multilayer perceptron, and deep neural network—are ensembled together to produce a single output. The results of numerical experiments show that the proposed ensemble classifier outperforms the benchmark models.