Abstract

In the face of advanced persistent threat actors, existing information technology (IT) defenses as well as some of the more recent operational technology (OT) defenses have been shown to become increasingly vulnerable, especially for critical infrastructure systems with well-established technical know-how. For example, data deception attacks have demonstrated their ability to mislead human operators and statistical detectors alike for a wide range of systems, e.g., electric grid, chemical and nuclear plants, etc. To combat this challenge, our previous work has introduced a new modeling paradigm, called covert cognizance (C²), serving as an active OT defense that allows a critical system to build self-awareness about its past performance, with the awareness parameters covertly embedded into its own state function, precluding the need for additional courier variables. Further, the embedding process employs one-time-pad randomization to blind artificial intelligence (AI)–based learning and ensures zero impact on system state. This paper employs one of the competing AI-based learning algorithms, i.e., the long short-term memory neural network in a supervised learning setting, to validate the C² embedding process. This is achieved by presenting the network with many labeled samples, distinguishing the original state function from the one containing the embedded self-awareness parameters. A nuclear reactor model is employed for demonstration.

摘要

面对先进的持续威胁行为者，现有的信息技术 (IT) 防御以及一些较新的操作技术 (OT) 防御已被证明变得越来越脆弱，尤其是对于具有完善技术知识的关键基础设施系统而言——如何。例如，数据欺骗攻击已经证明它们有能力误导人类操作员和统计检测器等广泛的系统，例如电网、化学和核电站等。为了应对这一挑战，我们之前的工作引入了一种新的模型范式，称为隐性认知（C ²)，作为一种主动的 OT 防御，允许关键系统对其过去的表现建立自我意识，并将意识参数秘密嵌入到其自己的状态函数中，从而无需额外的信使变量。此外，嵌入过程采用一次性随机化来进行基于人工智能 (AI) 的盲目学习，并确保对系统状态的影响为零。本文采用一种竞争的基于 AI 的学习算法，即监督学习环境中的长短期记忆神经网络，来验证 C ²嵌入过程。这是通过向网络呈现许多标记样本来实现的，将原始状态函数与包含嵌入的自我意识参数的状态函数区分开来。核反应堆模型用于演示。