DDS-Cerberus:票务性能实验与分析

Andrew T. Park, Richard Dill, D. Hodson, Wayne C. Henry
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引用次数: 3

摘要

数据分发服务(DDS)是一种发布-订阅中间件,用于在实时系统、生产环境和小型嵌入式平台之间分发数据。在DDS中,节点至少有一个发布服务器或订阅服务器。发布者和订阅者使用唯一的主题来发送和接收消息。如果发布服务器的消息引用了从发布服务器发送的相同主题,则每个订阅服务器都有权读取该消息。此功能支持实时通信,但会牺牲安全性,例如模拟攻击。本文详细介绍、测试和评估了将Ker-beros票务系统与DDS集成在一起的分布式通信协议DDS- cerberus (DDS- c)。DDS-C将Kerberos身份验证和票证检索与发布者和订阅者集成在一起。实验有六个参数,每个参数的发布者与订阅者比例为2:1。性能测试修改消息字节大小以模拟.txt和.mp3文件:10 KB、100 KB、1 MB、5 MB、10 MB和20 MB。功能和性能的实验指标是有线环境中的每秒消息数和延迟。实验采用ROS 2 (Robot Operating System)作为实验平台。基线的初始测试在不修改DDS的情况下进行,随后的测试在修改DDS- c的情况下进行。结果表明,由于票务组件,DDS- c通过防止模拟攻击来提高DDS安全性,同时与基线结果相比,可以忽略不计地提高平均处理速度。
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DDS-Cerberus: Ticketing Performance Experiments and Analysis
Data Distribution Service (DDS) is a publish-subscribe middleware used to distribute data between real-time systems, production environments, and small embedded plat-forms. In DDS, Nodes have at least one Publisher or Subscriber. Publishers and Subscribers use unique Topics to send and receive messages. Each Subscriber has permission to read the Publisher’s message if it references the same Topic sent from the Publisher. This capability supports real-time communication, sacrificing security, such as impersonation attacks.This paper details, tests, and evaluates DDS-Cerberus (DDS-C), a novel distributed communication protocol integrating Ker-beros ticketing system with DDS. DDS-C integrates Kerberos au-thentication and Ticket retrieval with Publishers and Subscribers. Experiments have six parameters each with a 2:1 Publisher to Subscriber ratio. Performance tests modify the message byte size to emulate .txt and .mp3 files: 10 KB, 100 KB, 1 MB, 5 MB, 10 MB, and 20 MB. Experiment metrics for functionality and performance are the messages per second and latency in a wired environment. Experiments utilize ROS 2 (Robot Operating System) as a testbed. Initial tests for a baseline are conducted without DDS modifications and subsequent tests with DDS-C modifications. The results reveal that due to the ticketing compo-nent, DDS-C increases DDS security by preventing impersonation attacks while negligibly increasing average processing compared to baseline results.
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