Network attacks are increasing day by day. In order to detect them, a system has been created, which actively detects intrusions and attacks in a network or an intranet. The system that detects these types of attacks and intrusions is called intrusion detection system (IDS). The attacks are of two kinds, known and unknown. The IDSs are able to protect against known attacks as they are designed specifically for them. As the usage of the Internet is growing every day, the attacks are increasing as well and all of them are not known to an IDS without proper upgradation, which is harmful as it will not be detected by the IDS and leave the system open to threats. Therefore, an IDS should not just detect the known attacks but even provide security from unknown attacks. Motivated by this, in this article, an ensemble-based IDS using XGBoost is presented. There has been previous research on the topic and with the help of improved technologies, it becomes possible to improve the efficiency and accuracy of the ensemble based IDS. This article proposes to present a scheme that shows the usage of XGBoost with ensemble based IDS can provide better results as XGBoost is based on the tree boosting machine learning algorithms, which helps dealing with a smoother “bias-variance” trade-off. The experiment is performed on the KDDCup99 dataset and the recorded accuracy of the proposed method through this experiment is 99.95%.

中文翻译：

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一种使用 XGBoost 的改进的基于集成的入侵检测技术

网络攻击与日俱增。为了检测它们，已经创建了一个系统，该系统主动检测网络或内联网中的入侵和攻击。检测这些类型的攻击和入侵的系统称为入侵检测系统 (IDS)。攻击分为已知和未知两种。IDS 能​​够抵御已知的攻击，因为它们是专门为它们设计的。随着互联网的使用每天都在增长，攻击也在增加，如果没有适当的升级，IDS 并不知道所有这些攻击，这是有害的，因为它不会被 IDS 检测到并使系统容易受到威胁. 因此，IDS 不仅应该检测已知的攻击，还应该提供防范未知攻击的安全性。受此启发，在本文中，介绍了使用 XGBoost 的基于集成的 IDS。之前已经有关于该主题的研究，并且在改进技术的帮助下，可以提高基于集成的 IDS 的效率和准确性。本文提出了一种方案，表明 XGBoost 与基于集成的 IDS 的使用可以提供更好的结果，因为 XGBoost 基于树提升机器学习算法，这有助于处理更平滑的“偏差-方差”权衡。实验在 KDDCup99 数据集上进行，通过该实验记录的方法的准确率为 99.95%。本文提出了一种方案，表明 XGBoost 与基于集成的 IDS 的使用可以提供更好的结果，因为 XGBoost 基于树提升机器学习算法，这有助于处理更平滑的“偏差-方差”权衡。实验在 KDDCup99 数据集上进行，通过该实验记录的方法的准确率为 99.95%。本文提出了一种方案，表明 XGBoost 与基于集成的 IDS 的使用可以提供更好的结果，因为 XGBoost 基于树提升机器学习算法，这有助于处理更平滑的“偏差-方差”权衡。实验在 KDDCup99 数据集上进行，通过该实验记录的方法的准确率为 99.95%。