一种无监督的深度可解释人工智能框架用于核反应堆信号中并发重放攻击的定位
An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals
AI总结：
本文提出了一种无监督的深度可解释人工智能框架，用于核反应堆信号中并发重放攻击的检测与识别。
摘要：
下一代先进核反应堆预计在尺寸和功率输出上都会更小，将大量依赖于完全数字化的仪器和控制系统。这些反应堆将生成大量的信息流，以多变量时间序列数据的形式同时传达各种非线性网络物理、过程、控制、传感器和运行状态。确保数据完整性以抵御欺骗攻击变得越来越重要，对于网络通信和安全可靠运行来说是一个要求。目前针对重放攻击的努力几乎普遍集中在水印或监督异常检测方法上，而没有进一步识别和表征异常的根本原因。此外，这些方法大多依赖于合成数据，具有不相关的高斯过程和测量噪声，或全状态反馈，或者局限于单变量信号、信号平稳性、线性二次调节器或其他线性时不变状态空间，这可能无法捕捉任何未建模的系统动态。在受监管的核网络物理系统领域，还需要更多关于重放攻击特性和预测可解释性使用真实数据的工作。在此，我们提出了一种基于自动编码器和定制窗口SHAP算法的无监督可解释人工智能框架，以完全表征实时重放攻击，即检测、源识别、时间和类型，随着动态时间演化的反应堆过程而增加的复杂性。所提出的XAI框架在多个真实世界数据集上进行了基准测试，这些数据集来自普渡大学的核反应堆PUR-1，最多有六个信号同时被重放。在所有情况下，XAI框架都能以95%或更好的准确性检测并识别被重放的信号源和数量以及虚假信息的持续时间。
英文摘要：
Next generation advanced nuclear reactors are expected to be smaller both in size and power output, relying extensively on fully digital instrumentation and control systems. These reactors will generate a large flow of information in the form of multivariate time series data, conveying simultaneously various non linear cyber physical, process, control, sensor, and operational states. Ensuring data integrity against deception attacks is becoming increasingly important for networked communication and a requirement for safe and reliable operation. Current efforts to address replay attacks, almost universally focus on watermarking or supervised anomaly detection approaches without further identifying and characterizing the root cause of the anomaly. In addition, these approaches rely mostly on synthetic data with uncorrelated Gaussian process and measurement noise and full state feedback or are limited to univariate signals, signal stationarity, linear quadratic regulators, or other linear-time invariant state-space which may fail to capture any unmodeled system dynamics. In the realm of regulated nuclear cyber-physical systems, additional work is needed on characterization of replay attacks and explainability of predictions using real data. Here, we propose an unsupervised explainable AI framework based on a combination of autoencoder and customized windowSHAP algorithm to fully characterize real-time replay attacks, i.e., detection, source identification, timing and type, of increasing complexity during a dynamic time evolving reactor process. The proposed XAI framework was benchmarked on several real world datasets from Purdue's nuclear reactor PUR-1 with up to six signals concurrently being replayed. In all cases, the XAI framework was able to detect and identify the source and number of signals being replayed and the duration of the falsification with 95 percent or better accuracy.
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