DC: Small: Scaling the Performance of Network Security Applications Using Massively Parallel Processing Array (MPPA) Architectures

DC：小型：使用大规模并行处理阵列 (MPPA) 架构扩展网络安全应用程序的性能

• 批准号: 1018886
• 负责人: Dipak Ghosal
• 金额: $ 39.1万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018886&HistoricalAwards=false
• 关键词: DC; Small; Scaling; Performance; Network

Performing network security applications such as malware detection, rule-based network intrusion detection, and covert channel detection at very high network line rates (10 Gbps now and soon scaling up to 40 to 100 Gbps) is critical to safeguarding enterprise networks. In this research project, we are investigating the applicability of MPPA (Massively Parallel Processing Array) architectures to scale packet processing and analysis tasks to meet the security challenges presented by next generation high-speed networks. Building upon our preliminary work, we are investigating parallel implementations of algorithms that are required in many different network security applications. These include 1) the K-means clustering algorithm used in traffic classification, 2) the entropy computation algorithm used in anomaly detection, 3) pattern matching used in rule-based network intrusion detection, and 4) encryption and decryption acceleration engines. We are investigate how these algorithms can be parallelized in a MPPA architecture with a large number of processors with limited memory and how the programmable processor interconnect can be leveraged to optimize the parallel implementation of algorithms. We are pursuing an experimental approach by building a testbed system to support both network traffic based and trace driven analyses. Through our research we expect to quantify the ability of MPPA architectures to scale data intensive computations for higher and higher line rates. The parallel algorithms and the implementation of the network security applications will be made available to other researchers. It is expected that graduate and undergraduate students involved in this project will obtain training in areas intersecting computer networks security, signal processing, and parallel processing.

在非常高的网络线速(现在是10 Gbps，很快会扩展到40到100 Gbps)下执行恶意软件检测、基于规则的网络入侵检测和隐蔽通道检测等网络安全应用程序，对于保护企业网络至关重要。在这项研究项目中，我们正在研究MPPA(大规模并行处理阵列)体系结构在扩展数据包处理和分析任务以应对下一代高速网络带来的安全挑战方面的适用性。在前期工作的基础上，我们正在调查许多不同网络安全应用程序所需的算法的并行实现。它们包括1)用于流量分类的K-Means聚类算法，2)用于异常检测的熵计算算法，3)用于基于规则的网络入侵检测的模式匹配，4)加解密加速引擎。我们正在研究如何在具有大量处理器和有限内存的MPPA体系结构中并行这些算法，以及如何利用可编程处理器互连来优化算法的并行实现。我们正在寻求一种实验方法，通过构建一个试验台系统来支持基于网络流量和跟踪驱动的分析。通过我们的研究，我们期望量化MPPA架构扩展数据密集型计算以获得越来越高的线速的能力。这些并行算法和网络安全应用的实现将可供其他研究人员参考。预计参与该项目的研究生和本科生将接受计算机网络安全、信号处理和并行处理交叉领域的培训。