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An active learning based TCM-KNN algorithm for supervised network intrusion detection

机译：一种基于主动学习的TCM-KNN监督网络入侵检测算法

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摘要

As network attacks have increased in number and severity over the past few years, intrusion detection is increasingly becoming a critical component of secure information systems and supervised network intrusion detection has been an active and difficult research topic in the field of intrusion detection for many years. However, it hasn't been widely applied in practice due to some inherent issues. The most important reason is the difficulties in obtaining adequate attack data for the supervised classifiers to model the attack patterns, and the data acquisition task is always time-consuming and greatly relies on the domain experts. In this paper, we propose a novel supervised network intrusion detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) machine learning algorithm and active learning based training data selection method. It can effectively detect anomalies with high detection rate, low false positives under the circumstance of using much fewer selected data as well as selected features for training in comparison with the traditional supervised intrusion detection methods. A series of experimental results on the well-known KDD Cup 1999 data set demonstrate that the proposed method is more robust and effective than the state-of-the-art intrusion detection methods, as well as can be further optimized as discussed in this paper for real applications.
机译：在过去的几年中，随着网络攻击的数量和严重性增加，入侵检测正日益成为安全信息系统的重要组成部分，而受监管的网络入侵检测已成为入侵检测领域多年来活跃且困难的研究课题。但是，由于一些固有的问题，它尚未在实践中得到广泛应用。最重要的原因是难以为监督分类器获取足够的攻击数据以对攻击模式进行建模，并且数据获取任务始终很耗时，并且严重依赖领域专家。在本文中，我们提出了一种新的基于监督的网络入侵检测方法，该方法基于TCM-KNN（K近邻的传递置信度机器）机器学习算法和基于主动学习的训练数据选择方法。与传统的监督入侵检测方法相比，它在使用更少的选择数据以及用于训练的选择特征的情况下，能够以较高的检测率，较低的误报率有效地检测异常。在著名的KDD Cup 1999数据集上进行的一系列实验结果表明，所提出的方法比最新的入侵检测方法更健壮和有效，并且可以如本文所述进行进一步优化用于实际应用。

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