论文标题
经验教训开发和扩展视觉分析解决方案，以调查骗局
Lessons Learned Developing and Extending a Visual Analytics Solution for Investigative Analysis of Scamming Activities
论文作者
论文摘要
网络安全分析师通过艰难地进行数千封电子邮件对话来寻找潜在的骗子活动和网络骗子网络，从而进行大型通信数据集，以执行调查分析。传统上，专家使用电子邮件客户端，数据库系统和文本编辑器来执行此调查。随着技术的出现，通过使用前沿数据可视化技术更有效地总结数据的精心设计工具已经出现。 Beagle [1]就是这样一种工具，它使用不同的面板可以看到大型通信数据，以使检查员有更好的机会找到骗局网络。本文是有关我们实施和改善Jay Koven等人所做工作的工作的报告。 [1]。我们已经通过实施提出和证明了一些我们认为可以更有效地分组和分析电子邮件数据的可视化效应。最后，我们还提出了一个案例研究，该案例研究显示了我们在现实世界中的工具的潜在用途。
Cybersecurity analysts work on large communication data sets to perform investigative analysis by painstakingly going over thousands of email conversations to find potential scamming activities and the network of cyber scammers. Traditionally,experts used email clients, database systems and text editors to perform this investigation. With the advent of technology,elaborate tools that summarize data more efficiently by using cutting edge data visualization techniques have come out. Beagle[1] is one such tool which visualizes the large communication data using different panels such that the inspector has better chances of finding the scam network. This paper is a report on our work to implement and improve the work done by Jay Koven et al. [1]. We have proposed and demonstrated via implementation, a few more visualizations that we feel would help in grouping and analyzing the e-mail data more efficiently. Lastly, we have also presented a case study that shows the potential use of our tool in a real-world scenario.
