Do you feel lucky?: a large-scale analysis of risk-rewards trade-offs in cyber security

Yan Shoshitaishvili, L. Invernizzi, Adam Doupé, G. Vigna
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引用次数: 14

Abstract

A crucial part of a cyber-criminal's job is to balance the risks and rewards of his every action. For example, an expert spammer will tune a bot's email-sending rate to achieve a good throughput with an acceptable risk of being detected. Then, such a cyber-criminal has to choose how to launder the money he made with spamming, and he will have to consider many options (money mules, Bitcoin, etc.) that will offer different returns and risks. Although understanding these trade-offs and coming as close as possible to their optimum is what discriminates winners and losers in the cyber-crime world, there has been little study on this matter, as setting up a large-scale study to study how cyber-criminals deal with these risk-reward trade-offs is challenging. Computer security competitions provide a great opportunity both to educate students and to study realistic cyber-security scenarios in a controlled environment. Looking to study the risk-reward trade-offs seen in real cyber-security incidents, we designed and hosted a novel format for a Capture the Flag cyber-security contest, involving 89 teams comprising over 1,000 students across the globe. In this paper, we describe the intuition, intent, and design of the contest. Additionally, we present an analysis of the data set collected, evaluate its effectiveness in modeling risk-reward behavior, examine the strategies of the competing teams, and estimate the effectiveness of such strategies.
你觉得幸运吗?对网络安全领域风险回报权衡的大规模分析
网络罪犯工作的一个关键部分是平衡他的每一个行动的风险和回报。例如,专业的垃圾邮件发送者将调整机器人的电子邮件发送率,以在可接受的被检测风险下实现良好的吞吐量。然后,这样一个网络罪犯必须选择如何洗钱,他通过垃圾邮件赚了钱,他将不得不考虑许多选择(钱骡子,比特币等),将提供不同的回报和风险。尽管在网络犯罪领域,理解这些权衡并尽可能接近其最佳状态是区分赢家和输家的关键,但关于这个问题的研究很少,因为建立一个大规模的研究来研究网络罪犯如何处理这些风险-回报权衡是具有挑战性的。计算机安全竞赛提供了一个很好的机会,既可以教育学生,又可以在受控环境中研究现实的网络安全场景。为了研究真实网络安全事件中的风险回报权衡,我们设计并举办了一场新颖的夺旗网络安全竞赛,共有89支队伍参加,其中包括来自全球的1000多名学生。在本文中,我们描述了比赛的直觉、意图和设计。此外,我们对收集的数据集进行了分析,评估了其在建模风险-回报行为方面的有效性,检查了竞争团队的策略,并估计了这些策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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