网络物理系统中的信息隐藏:利用SWAT数据集传感器数据嵌入、检索和检测的挑战

Kevin Lamshöft, T. Neubert, Christian Krätzer, C. Vielhauer, J. Dittmann
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引用次数: 8

摘要

在本文中,我们提出了一种信息隐藏方法,该方法将适用于通过利用历史数据库中过程数据的长期存储来渗透工业控制系统(ICS)的敏感信息。我们将展示如何将隐藏的消息嵌入到传感器测量中,以及如何通过访问历史记录异步检索。我们以iTrust安全水处理(SWAT)数据集的流量和水位传感器为例评估了这种方法。为了从特定的掩蔽通道(传感器及其传输的数据)进行概括,我们反思了在创建网络隐蔽通道的信息隐藏场景中出现的一般挑战,并讨论了掩蔽通道选择和发送方接收方同步以及时间方面的问题,例如网络物理系统(CPS)中隐藏信息的潜在持久性。为了进行经验评估,我们设计并实现了一个隐蔽信道,该信道利用不同的嵌入策略对传感器测量中的噪声执行自适应方法,从而产生动态容量和带宽选择以降低检测概率。该评估结果表明,使用这种方法,在长期大规模攻击中窃取敏感信息确实是可能的。此外,我们为引入的隐藏通道提出了两种检测方法,并使用多个测试数据集和不同参数对我们的检测器进行了广泛的评估。我们确定在假阳性率(FPR)为0%的情况下,测试数据的检测准确率高达87.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information Hiding in Cyber Physical Systems: Challenges for Embedding, Retrieval and Detection using Sensor Data of the SWAT Dataset
In this paper, we present an Information Hiding approach that would be suitable for exfiltrating sensible information of Industrial Control Systems (ICS) by leveraging the long-term storage of process data in historian databases. We show how hidden messages can be embedded in sensor measurements as well as retrieved asynchronously by accessing the historian. We evaluate this approach at the example of water-flow and water-level sensors of the Secure Water Treatment (SWAT) dataset from iTrust. To generalize from specific cover channels (sensors and their transmitted data), we reflect upon general challenges that arise in such Information Hiding scenarios creating network covert channels and discuss aspects of cover channel selection and and sender receiver synchronisation as well as temporal aspects such as the potential persistence of hidden messages in Cyber Physical Systems (CPS). For an empirical evaluation we design and implement a covert channel that makes use of different embedding strategies to perform an adaptive approach in regards to the noise in sensor measurements, resulting in dynamic capacity and bandwidth selection to reduce detection probability. The results of this evaluation show that, using such methods, the exfiltration of sensible information in long-term scaled attacks would indeed be possible. Additionally, we present two detection approaches for the introduced hidden channel and carry out an extensive evaluation of our detectors with multiple test data sets and different parameters. We determine a detection accuracy of up to 87.8% on test data at a false positive rate (FPR) of 0%.
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