An improved ensemble based intrusion detection technique usingXGBoost
An improved ensemble based intrusion detection technique usingXGBoost
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DOI：
10.1002/ett.4076
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发表时间：
2020-08-07
影响因子：
3.6
通讯作者：
Bhati, Nitesh Singh
中科院分区：
文献类型：
--
作者：
Bhati, Bhoopesh Singh;Chugh, Garvit;Bhati, Nitesh Singh
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被称为入侵检测系统（IDS）。这些攻击有两种，已知的和未知的。IDS能够防止已知的攻击，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，如果没有适当的预防，所有这些攻击都不会被IDS所知，这是有害的，因为它不会被IDS检测到，并使系统受到威胁。因此，入侵检测系统不仅要检测已知的攻击，还要提供针对未知攻击的安全保护。基于此，本文提出了一种基于XGBoost的集成入侵检测系统。已有相关的研究成果，随着技术的不断改进，基于集成的入侵检测系统的效率和准确性有了进一步的提高。本文提出了一种方案，表明XGBoost与基于集成的IDS一起使用可以提供更好的结果，因为XGBoost是基于树提升机器学习算法的，这有助于处理更平滑的“偏差-方差”权衡。在KDDCup 99数据集上进行了实验，通过实验，所提出的方法的记录准确率为99.95%。
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%.