Information flow for security in control systems
Information flow for security in control systems
复制标题
DOI：
10.1109/cdc.2016.7799044
复制
发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Sean Weerakkody;B. Sinopoli;S. Kar;Anupam Datta
中科院分区：
文献类型：
--
作者：
Sean Weerakkody;B. Sinopoli;S. Kar;Anupam Datta
本文认为信息流分析的发展，以支持弹性设计和主动检测的对手在网络物理系统（CPS）。CPS的安全性虽然得到了很好的研究，但仍存在碎片化问题。在本文中，我们认为控制系统作为一个抽象的CPS。在这里，我们使用信息流分析，一套完善的软件安全开发的方法，以获得一个统一的框架，捕获和扩展控制系统安全的结果。具体来说，我们提出了Kullback Liebler（KL）分歧作为信息流的因果度量，它量化了对抗性输入对传感器输出的影响。我们表明，所提出的措施的特点，具体的攻击策略的控制系统的弹性KL分歧的最佳检测。然后，我们将信息流与对手可以绕过检测的隐形攻击场景相关联。最后，本文研究了主动检测机制，其中防御者智能地操纵控制输入或系统本身，以从攻击者的恶意行为中引出信息流。在所有以前的情况下，我们证明了调查和扩展现有的结果，通过建议的信息流分析的能力。
This paper considers the development of information flow analyses to support resilient design and active detection of adversaries in cyber physical systems (CPS). CPS security, though well studied, suffers from fragmentation. In this paper, we consider control systems as an abstraction of CPS. Here, we use information flow analysis, a well established set of methods developed in software security, to obtain a unified framework that captures and extends results in control system security. Specifically, we propose the Kullback Liebler (KL) divergence as a causal measure of information flow, which quantifies the effect of adversarial inputs on sensor outputs. We show that the proposed measure characterizes the resilience of control systems to specific attack strategies by relating the KL divergence to optimal detection. We then relate information flows to stealthy attack scenarios where an adversary can bypass detection. Finally, this article examines active detection mechanisms where a defender intelligently manipulates control inputs or the system itself to elicit information flows from an attacker's malicious behavior. In all previous cases, we demonstrate an ability to investigate and extend existing results through the proposed information flow analyses.