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Tree-based string pattern matching on FPGAs

机译:FPGA上基于树的字符串模式匹配

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摘要

Network intrusion detection systems (NIDSs) monitor Internet Protocol (IP) traffic to detect anomalous and malicious activities on a network. Despite the plethora of studies in this field, hardware-based string matching engines that can accommodate the advancements in optical networking technology are still in high demand. Furthermore, memory efficient data structures to store intrusion patterns have recently received a great deal of research attention. In this paper, we introduce a tree-based pattern matching (TPM) scheme that comprises a forest of Binary Search Tree (BST) data structures and an accommodating high-throughput multi-pipelined architecture for scalable string matching on hardware. To improve the resource efficiency in hardware implementations, we enhanced TPM scheme (extended-TPM) with two novel tree structures, namely BST-epsilon (BST epsilon) and hierarchical BST (H-BST). Our entire design accomplishes a memory efficiency of 1.07 bytes/char for the latest Snort dictionary. Utilizing a state-of-the-art Field Programmable Gate Arrays (FPGAs), TPM architecture can sustain a throughput of 2.7 Gbps. (C) 2015 Elsevier Ltd. All rights reserved.
机译:网络入侵检测系统(NIDS)监视Internet协议(IP)流量,以检测网络上的异常和恶意活动。尽管在该领域进行了大量研究,但是仍然对能够适应光网络​​技术的进步的基于硬件的字符串匹配引擎有很高的要求。此外,用于存储入侵模式的高效内存数据结构近来受到了大量研究关注。在本文中,我们介绍了一种基于树的模式匹配(TPM)方案,该方案包括二进制搜索树(BST)数据结构林和可容纳的高吞吐量多管道体系结构,用于硬件上的可伸缩字符串匹配。为了提高硬件实现中的资源效率,我们使用两种新颖的树结构(即BST-epsilon(BST epsilon)和分层BST(H-BST))增强了TPM方案(extended-TPM)。我们的整个设计为最新的Snort字典实现了1.07字节/字符的存储效率。利用最先进的现场可编程门阵列(FPGA),TPM架构可以维持2.7 Gbps的吞吐量。 (C)2015 Elsevier Ltd.保留所有权利。

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