With the rapid growth and advancement in technology, we are becoming more and more dependent on the internet for our day-to-day work. As a result of this, we have become an easy target for the attackers. More the usage of the internet more we are vulnerable to threats. In this scenario, the need for anti-virus or some system that can help in detecting the threats or attacks is also growing rapidly. The answer to this problem is a system that when installed on a network shall be able to detect Intrusions of any sort. Such a system can be called an IDS (Intrusion Detection System). This system is deployed on any network to keep a track of the traffic and to monitor any kind of mutation or deviations from regular traffic patterns. Many kinds of research are still ongoing in this field to develop a system that has not only a better error detection rate but also can introduce preventive measures as soon as a threat is detected. This paper proposes a system for detecting the attacks using machine learning where the Recursive Feature Elimination (RFE) technique is applied when the irrelevant features are there in the dataset. This technique helps remove any sort of redundancy in the KDD CUP99 dataset which is a standard dataset for network security and intrusion detection. Then for the achieved set of features a confusion matrix is generated and the classification of records is done using decision tree, support vector machine, and an ensemble classifier random forest in the form of discriminant analysis. When compared with other methodologies our approach holds a good classification rate for all the classes of attacks of the KDD CUP 99 dataset.

随着技术的快速发展和进步，我们的日常工作越来越依赖互联网。因此，我们很容易成为攻击者的目标。互联网的使用越多，我们就越容易受到威胁。在这种情况下，对防病毒软件或一些可以帮助检测威胁或攻击的系统的需求也在迅速增长。这个问题的答案是一个系统，当安装在网络上时，它应该能够检测任何类型的入侵。这样的系统可以称为IDS（入侵检测系统）。该系统部署在任何网络上，以跟踪流量并监控与常规流量模式的任何类型的突变或偏差。该领域的许多研究仍在进行中，以开发一种不仅具有更好的错误检测率而且可以在检测到威胁时立即采取预防措施的系统。本文提出了一种使用机器学习检测攻击的系统，其中当数据集中存在不相关的特征时，应用递归特征消除 (RFE)技术。该技术有助于消除 KDD CUP99 数据集中的任何类型的冗余，该数据集是用于网络安全和入侵检测的标准数据集。然后，对于获得的特征集，生成混淆矩阵，并使用决策树、支持向量机和集成分类器随机森林以判别分析的形式对记录进行分类。与其他方法相比，我们的方法对 KDD CUP 99 数据集的所有攻击类别都具有良好的分类率。