SpearSim-V2: Synthetic Task Environment for Evaluating Attacker Behaviors

Elaheh Mehrabi, Tianhao Xu, Prashanth Rajivan
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Abstract

Despite extensive research on phishing, a severe lack of work centered on attackers has resulted in a limited understanding of the adversarial behaviors conducive to attack success and failures. This work describes a novel method for conducting controlled laboratory studies of cognitive vulnerabilities that attackers experience during the design and execution phases of spear-phishing attacks. Based on the SpearSim platform, the new simulation environment integrates cognitive agents that model and predict end-user responses to spear-phishing attacks. This advancement to SpearSim allows the generation of real-time, automated, “human-like” responses to simulated spear-phishing attacks. This enables the execution of experiments focused on attackers and attacker behaviors. We describe the proposed simulation framework, provide details about the implemented simulation environment, and present results to evaluate the performance of the simulation environment. Compared to the earlier version of SpearSim involving human end-users, the new approach generates responses at a much faster rate (3 times faster than human end-users) and importantly with less variance in the time to respond. The cognitive agents used in the simulation predicted human responses to phishing and spear-phishing attackers with moderate accuracy (about 60%). Our proposed method intends to provide an effective and robust way to conduct laboratory experiments on spear-phishing attacks and further understand attackers' decision-making processes that could be exploited to thwart future attacks.
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SpearSim-V2:用于评估攻击者行为的综合任务环境
尽管对网络钓鱼进行了广泛的研究,但严重缺乏以攻击者为中心的工作,导致对有助于攻击成功和失败的对抗行为的理解有限。这项工作描述了一种对攻击者在鱼叉式网络钓鱼攻击的设计和执行阶段所经历的认知漏洞进行受控实验室研究的新方法。基于SpearSim平台,新的仿真环境集成了认知代理,可以模拟和预测最终用户对鱼叉式网络钓鱼攻击的反应。SpearSim的这一进步允许对模拟鱼叉式网络钓鱼攻击生成实时、自动化的“类人”响应。这样就可以执行针对攻击者和攻击者行为的实验。我们描述了提出的仿真框架,提供了有关实现的仿真环境的细节,并给出了评估仿真环境性能的结果。与涉及人类最终用户的早期版本的SpearSim相比,新方法以更快的速度生成响应(比人类最终用户快3倍),重要的是响应时间的变化更小。模拟中使用的认知代理以中等精度(约60%)预测人类对网络钓鱼和鱼叉式网络钓鱼攻击者的反应。我们提出的方法旨在提供一种有效而稳健的方法来对鱼叉式网络钓鱼攻击进行实验室实验,并进一步了解攻击者的决策过程,这些决策过程可以用来挫败未来的攻击。
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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