Machine learning (ML) has demonstrated great potential to revolutionize the networking field. In this paper, we present a large-scale empirical study to evaluate the effectiveness of state-of-the-art ML algorithms for network application security. In our experiments, six classical ML algorithms and three neural network algorithms are evaluated over three networking datasets, KDDCup 99, NSL-KDD, and ADFA IDS 2017. Measurements are made between the non-optimized and optimized versions of ML algorithms. Furthermore, various training and testing ratios are experimented to assess each algorithm’s optimal performance. The results revealed that optimizing ML algorithms could help achieve better performance in detecting networking attacks. In particular, the Decision Tree proved to be the most accurate and fastest algorithm in the classical ML while the Recurrent Neural Network achieved the best performance among neural network algorithms.

机器学习（ML）已显示出巨大的潜力，可以彻底改变网络领域。在本文中，我们提出了一项大规模的实证研究，以评估最新的ML算法对于网络应用程序安全性的有效性。在我们的实验中，在三个网络数据集KDDCup 99，NSL-KDD和ADFA IDS 2017上评估了六种经典ML算法和三种神经网络算法。在ML算法的非优化版本和优化版本之间进行了测量。此外，实验了各种训练和测试比率，以评估每种算法的最佳性能。结果表明，优化ML算法可以帮助实现更好的检测网络攻击的性能。特别是，