Towards Enhancing Inter-Domain Routing Security With Visualization and Visual Analytics

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jingwei Tang;Guodao Sun;Jiahui Chen;Gefei Zhang;Qi Jiang;Yanbiao Li;Guangxing Zhang;Jian Liu;Haixia Wang;Ronghua Liang
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引用次数: 0

Abstract

In the complex landscape of the Internet, inter-domain routing systems are essential for ensuring seamless connectivity and reachability across autonomous systems. However, the lack of dependable security validation mechanisms in these systems poses persistent challenges. Vulnerabilities such as prefix hijacking, path forgery, and route leakage not only compromise network operators and users, but also threaten the stability and accessibility of the Internet’s core infrastructure. To address this, visualization and visual analytics techniques are adept at identifying and detecting security threats, offering network administrators effective methods to monitor and maintain network operations. This paper presents a comprehensive survey of the state-of-the-art research in visualization and visual analytics for inter-domain routing security. We delineate four scenarios for tasks analysis in network visualization: monitoring, detection, verification, and discovery. Each category is explored in detail, focusing on the employed data sources and visualization techniques. Several key findings are presented at the end of each category, aimed at providing researchers and practitioners with research inspiration. Furthermore, we examine the trends of academic interest observed in recent decades and propose potential directions for future research in visual analytics pertaining to Internet infrastructure security.
利用可视化和可视化分析增强域间路由安全性
在复杂的互联网环境中,域间路由系统对于确保自治系统之间的无缝连接和可达性至关重要。然而,在这些系统中缺乏可靠的安全验证机制带来了持续的挑战。前缀劫持、路径伪造、路由泄漏等漏洞不仅危及网络运营商和用户,而且威胁到互联网核心基础设施的稳定性和可访问性。为了解决这个问题,可视化和可视化分析技术擅长于识别和检测安全威胁,为网络管理员提供了监视和维护网络操作的有效方法。本文对域间路由安全可视化和可视化分析的最新研究进行了全面的综述。我们描述了网络可视化中任务分析的四种场景:监控、检测、验证和发现。每个类别都进行了详细的探讨,重点是所使用的数据源和可视化技术。在每个类别的末尾提出了几个关键发现,旨在为研究人员和实践者提供研究灵感。此外,我们研究了近几十年来观察到的学术兴趣趋势,并提出了与互联网基础设施安全相关的视觉分析未来研究的潜在方向。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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