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首页> 外文期刊>International journal of parallel programming >An Intrusion Detection Framework Based on Hybrid Multi-Level Data Mining
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An Intrusion Detection Framework Based on Hybrid Multi-Level Data Mining

机译：基于混合多级数据挖掘的入侵检测框架

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摘要

With the dramatic opening-up of network, network security becomes a severe social problem with the rapid development of network technology. Intrusion Detection System (IDS) is an innovative and proactive network security technology, which becomes a hot topic in both industry and academia in recent years. There are four main characteristics of intrusion data that affect the performance of IDS including multicomponent, data imbalance, time-varying and unknown attacks. We propose a novel IDS framework called HMLD to address these issues, which is an exquisite designed framework based on Hybrid Multi-Level Data Mining. In this paper, we use KDDCUP99 dataset to evaluate the performance of HMLD. The experimental results show that HMLD can reach 96.70% accuracy which is nearly 1% higher than the recent proposed optimal algorithm SVM+ELM+Modified K-Means. In details, HMLD greatly increased the detection accuracy of DoS attacks and R2L attacks.
机译：随着网络的急剧开放，随着网络技术的飞速发展，网络安全成为一个严重的社会问题。入侵检测系统（IDS）是一种创新且主动的网络安全技术，近年来已成为业界和学术界的热门话题。入侵数据的四个主要特征会影响IDS的性能，包括多分量，数据不平衡，时变和未知攻击。我们提出了一个新颖的IDS框架HMLD来解决这些问题，这是一个基于混合多级数据挖掘的精美设计框架。在本文中，我们使用KDDCUP99数据集来评估HMLD的性能。实验结果表明，HMLD可以达到96.70％的精度，比最近提出的最优算法SVM + ELM + Modified K-Means高出近1％。具体而言，HMLD大大提高了DoS攻击和R2L攻击的检测准确性。

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