With the deep research on network security field and the extensive application of network service, the demand for network attack raw dataset is increasing.
随着网络安全领域研究的深入和网络服务的广泛应用，人们对网络攻击数据集需求量逐渐增大。
The experiments show that IBN-M algorithm can learn comparatively accurate network from the extremely large dataset. IBN-M is an interesting improvement for incremental learning Bayesian Network.
实验结果表明IBN-M算法在数据缺失下贝叶斯网络的增量学习中确实能够学出相对精确的网络模型，该算法也是对贝叶斯网络增量学习方面的一个必要的补充。
The experiments show that IBN-M algorithm can learn comparatively accurate network from the extremely large dataset. IBN-M is an interesting improvement for incremental learning Bayesian Network.
实验结果表明IBN-M算法在数据缺失下贝叶斯网络的增量学习中确实能够学出相对精确的网络模型，该算法也是对贝叶斯网络增量学习方面的一个必要的补充。
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