An improved ensemble based intrusion detection technique usingXGBoost

Abstract

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 KDD-Cup99 dataset and the recorded accuracy of the proposed method through this experiment is 99.95%.

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