Impact of computer users on cyber defense strategies

C. D. Colvett, Mikel D. Petty, J. Bland
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Abstract

Cybersecurity research often focuses primarily or exclusively on the interactions between the attacker, trying to exploit the computer system, and the defender, trying to protect it. However, including the computer users is important because the users’ requirements are the reason the computer system exists.An extension of the Petri net formalism, Petri Nets with Players, Strategies, and Costs (PNPSC) was used to model cyberattacks described in the MITRE Common Attack Pattern Enumeration and Classification database. PNPSC models include the attacker, defender, and computer user as “players” attempting to achieve competing goals. Each player can observe the current marking of a subset of the PNPSC net's places and change the stochastic firing rates of a subset of the net's transitions in order to achieve their goals. A mapping between the markings of a player's observable places and the desired firing rates of player's controllable transitions is the player's strategy.A reinforcement learning algorithm was integrated with PNPSC models of three cyberattack patterns to learn strategies for the defender in simulations both with and without a representation of the computer user. A simulation experiment showed that the defender's reward was lower and the defender's learned strategy was different when the user was represented. A second simulation experiment and statistical analysis confirmed that the differences were not due simply to randomness. With the user represented, the system defender must balance security against usability. This research provides a more complete cyberattack model and shows that user models are important in future cybersecurity simulation.
计算机用户对网络防御战略的影响
网络安全研究通常主要或仅关注试图利用计算机系统的攻击者与试图保护计算机系统的防御者之间的互动。然而,将计算机用户包括在内非常重要,因为用户的需求是计算机系统存在的原因。Petri 网形式主义的扩展--带参与者、策略和成本的 Petri 网(PNPSC)被用于对 MITRE 通用攻击模式枚举和分类数据库中描述的网络攻击进行建模。PNPSC 模型将攻击者、防御者和计算机用户作为 "参与者",试图实现相互竞争的目标。每个玩家都能观察到 PNPSC 网络中一个子集的当前标记,并改变网络中一个子集转换的随机发射率,以实现自己的目标。将强化学习算法与三种网络攻击模式的 PNPSC 模型相结合,在有计算机用户代表和无计算机用户代表的模拟中学习防御者的策略。模拟实验表明,在有用户代表的情况下,防御方的奖励较低,防御方学习到的策略也不同。第二次模拟实验和统计分析证实,这种差异并不只是由于随机性造成的。在用户被代表的情况下,系统防御者必须在安全性和可用性之间取得平衡。这项研究提供了一个更完整的网络攻击模型,并表明用户模型在未来的网络安全模拟中非常重要。
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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