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首页> 外文期刊>IEEE systems journal >Slow-Paced Persistent Network Attacks Analysis and Detection Using Spectrum Analysis
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Slow-Paced Persistent Network Attacks Analysis and Detection Using Spectrum Analysis

机译：慢速持久性网络攻击的分析和频谱分析检测

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摘要

A slow-paced persistent attack, such as slow worm or bot, can bewilder the detection system by slowing down their attack. Detecting such attacks based on traditional anomaly detection techniques may yield high false alarm rates. In this paper, we frame our problem as detecting slow-paced persistent attacks from a time series obtained from network trace. We focus on time series spectrum analysis to identify peculiar spectral patterns that may represent the occurrence of a persistent activity in the time domain. We propose a method to adaptively detect slow-paced persistent attacks in a time series and evaluate the proposed method by conducting experiments using both synthesized traffic and real-world traffic. The results show that the proposed method is capable of detecting slow-paced persistent attacks even in a noisy environment mixed with legitimate traffic.
机译：慢速持续攻击（例如慢速蠕虫或漫游器）可能会通过减慢其攻击速度而使检测系统困惑。基于传统的异常检测技术检测到此类攻击可能会产生较高的虚警率。在本文中，我们将问题构架为从网络跟踪获得的时间序列中检测慢速持续攻击。我们专注于时间序列频谱分析，以识别可能表示时域持续活动发生的特殊频谱模式。我们提出一种方法来自适应地检测时间序列中的慢节奏持续性攻击，并通过使用合成流量和真实流量进行实验来评估该方法。结果表明，所提出的方法即使在嘈杂的环境中混合合法流量，也能够检测到慢速持续攻击。

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