AnubisFlow:分布式拒绝服务攻击分类的特征提取器

Alan Barzilay, Caio L. Martinelli, M. N. Lima, D. Batista, R. Hirata
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引用次数: 0

摘要

DDoS攻击的检测和缓解需要系统以实时捕获的方式对传入的网络流进行分析和处理。在这种情况下,有效的分析依赖于一组良好的特征来对流量进行分类。考虑到这一目标,我们提出了一种基于一组新特征的技术,这些特征计算成本低,并且对数据流具有描述性。此外,该技术在许多时刻考虑流,而不仅仅是在它们完成时。我们通过创建决策树模型和逻辑回归来分析其预测性能,分别达到99.98%和95.99%的科恩Kappa系数。本着最近研究结果可重复性的趋势,我们将该提案集成到一个名为AnubisFlow的开源工具中。此外,我们对模型的分析可以作为开放数据提供给科学界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AnubisFlow: A Feature Extractor for Distributed Denial of Service Attack Classification
The detection and mitigation of DDoS attacks require a system to analyze and process the incoming network flow in a live capture manner. In this scenario, an efficient analysis depends on a good set of features to classify the traffic. With this goal in mind, we propose a technique based on a new set of features that are computationally inexpensive and descriptive of the data stream. Moreover, the technique considers the flows in many moments, not only when they are finished. We analyze its predicting performance by creating a decision tree model and a logistic regression, which achieved 99.98% and 95.99% Cohen’s Kappa coefficient, respectively. In spirit with the recent trend toward reproducibility of research results, we integrate the proposal in an open-source tool called AnubisFlow. Also, our analysis for the models is available as open data to the scientific community.
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