基于AR模型的SYN Flood攻击检测

Rania A. Ghazy, El-Sayed M. El-Rabaie, M. Dessouky, N. El-Fishawy, F. E. El-Samie
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引用次数: 0

摘要

由于自回归(AR)建模方法的复杂特性,它在大多数异常检测过程中都有应用。本文扩展了AR建模的概念,为网络流量的数据和控制平面数据包计数之间的估计自相关性创建了模型。这些模型被注入了包含SYN flood攻击的异常流量。这些场景中的估计残差用作攻击的指标。仿真结果表明,该方法能够成功地进行攻击检测。
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SYN Flood Attack Detection Usiing AR Model
Due to the sophisticated characteristics of auto-regressive (AR) modeling approach, it finds applications in most anomaly detection processes. This paper extends the concept of AR modeling to create models for the estimated auto-correlation between data and control planes packet counts of the network traffic. These models are fed with the anomaly traffic containing SYN flood attack. The estimated residuals in these scenarios are used as indicators for the attacks. Simulation results revealed the success of attack detection using the proposed approach.
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