Deep learning approaches can more effectively detect network intrusions compared to traditional algorithms like SVM and random forest. The author evaluates these algorithms on intrusion detection datasets and finds DNN performs better, especially for dynamic attacks with changing parameters. DNN is able to filter training data through its hidden layers to form a more accurate predictive model, outperforming other algorithms on testing data classification.