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Tearing Down the Face of Algorithmic Complexity Attacks for DPI Engines

机译:揭露DPI引擎的算法复杂性攻击面

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摘要

Deep Packet Inspection (DPI) is the core of security devices, such as NIDS, NIPS, which is also an important target of the adversary. The vulnerability of DPI engine is that it relies heavily on pattern matching algorithms, which consume a lot of system resources. In order to make denial of service of DPI, the adversary leverages string repetitions to perform algorithmic complexity attacks. In this paper, we propose an attack identification method for automata and design three defensive strategies. Our attack identification method adopts a two-step threshold detection method, while defensive mechanisms include dropping, transferring and rescheduling the traffic. And the rescheduling traffic based on multi-core platform is a parallelization problem. To solve this problem, this paper proposes a traffic exchange strategy between threads, so that the attack traffic is allocated to dedicated threads. We demonstrate the effectiveness of our method by checking the packet loss rate of NIC and monitoring the utilization of CPU and memory. Upon different attack intensity, our experiments show a throughput boost of up to 11%-60% by comparing with the original system, and 4%-14% with the Level-1 threshold detection. In addition, the false negative rate under the diversified attack scenarios is lower than the original system and Level-1 threshold detection.
机译:深度数据包检查(DPI)是安全设备的核心,例如NIDS,NIPS,这也是攻击者的重要目标。 DPI引擎的漏洞在于它严重依赖于模式匹配算法,这会消耗大量系统资源。为了拒绝提供DPI服务,攻击者利用字符串重复执行算法复杂性攻击。本文提出了一种针对自动机的攻击识别方法,并设计了三种防御策略。我们的攻击识别方法采用两步阈值检测方法,而防御机制包括丢弃,传输和重新安排流量。而基于多核平台的流量调度是一个并行化问题。为了解决这个问题,本文提出了线程间的流量交换策略,将攻击流量分配给专用线程。我们通过检查NIC的丢包率并监视CPU和内存的利用率来证明我们方法的有效性。在不同的攻击强度下,我们的实验表明,与原始系统相比,吞吐率提高了11%,达到-60%,而对于Level-1阈值检测,吞吐量提高了%-14%。另外,多种攻击场景下的误报率均低于原始系统和1级阈值检测。

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