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首页> 外文会议>International Conference on VLSI Design;International Conference on Embedded Systems >BRAIN: BehavioR Based Adaptive Intrusion Detection in Networks: Using Hardware Performance Counters to Detect DDoS Attacks
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BRAIN: BehavioR Based Adaptive Intrusion Detection in Networks: Using Hardware Performance Counters to Detect DDoS Attacks

机译：脑：网络中基于行为的自适应入侵检测：使用硬件性能计数器检测DDoS攻击

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摘要

Denial-of-Service (DoS) and Distributed Denial-of Service (DDoS) attacks account for one third of all service downtime incidents. Current DoS/DDoS attacks are not only limited to knocking down online services, but they also disguise other malicious attacks such as delivering malware, data-theft, wire fraud and even extortion. Detection of these attacks is predominantly based on the packet data and metrics derived only from packets. This work proposes a host based DDoS detection framework called BRAIN: BehavioR based Adaptive Intrusion detection in Networks. BRAIN leverages already available Hardware Performance Counters in modern processors to model the application behavior using low-level hardware events. BRAIN combines network statistics and modeled application behavior to detect DDoS attacks using machine learning. Our experiments show that BRAIN can detect multiple types of DDoS attacks, including those are undetectable by existing tools with an accuracy of 99.8% and a false alarm rate of 0%.
机译：拒绝服务（DoS）和分布式拒绝服务（DDoS）攻击占所有服务停机事件的三分之一。当前的DoS / DDoS攻击不仅限于关闭在线服务，而且还掩盖了其他恶意攻击，例如传递恶意软件，数据盗窃，电汇欺诈甚至勒索。这些攻击的检测主要基于数据包数据和仅从数据包得出的指标。这项工作提出了一种基于主机的DDoS检测框架，称为BRAIN：网络中基于BehavioR的自适应入侵检测。 BRAIN利用现代处理器中已经可用的硬件性能计数器来使用低级硬件事件对应用程序行为进行建模。 BRAIN结合了网络统计信息和建模的应用程序行为，以使用机器学习检测DDoS攻击。我们的实验表明，BRAIN可以检测多种类型的DDoS攻击，包括现有工具无法检测到的DDoS攻击，其准确性为99.8％，错误警报率为0％。

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