RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process

故障检测与诊断中的对抗攻击与防御：基于田纳西-伊斯曼过程的综合基准研究

Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

本文基于田纳西-伊斯曼过程数据集，系统评估了三种神经网络在六种对抗攻击下的脆弱性，比较五种防御方法，并提出组合多种防御的新保护方案，为工业故障诊断中的机器学习安全提供基准与见解。

Grounds for Suspicion: Physics-based Early Warnings for Stealthy Attacks on Industrial Control Systems

Stealthy attacks on Industrial Control Systems can cause significant damage while evading detection. In this paper, instead of focusing on the detection of stealthy attacks, we aim to provide early warnings to operators, in order to avoid physical damage and preserve in advance data that may serve as an evidence during an investigation. We propose a framework to provide grounds for suspicion, i.e. preliminary indicators reflecting the likelihood of success of a stealthy attack. We propose two grounds for suspicion based on the behaviour of the physical process: (i) feasibility of a stealthy at…

通过强化数字孪生沙箱内的共进化军备竞赛实现自主网络安全

Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox

本文提出ARC框架，通过数字孪生沙箱内的共进化军备竞赛，实现工业控制系统对新型攻击的高效检测与防御，提升网络安全的动态适应能力。

On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems Using Multivariate Statistical Process Control

Process Control Systems (PCSs) are the operating core of Critical Infrastructures (CIs). As such, anomaly detection has been an active research field to ensure CI normal operation. Previous approaches have leveraged network level data for anomaly detection, or have disregarded the existence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In this paper we present an anomaly detection and diagnostic system based on Multivariate Statistical Process Control (MSPC), that aims to distinguish between attacks and disturbances. For this end,…

Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation

In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these to generate combinatorial attacks to damage the safety of the system, and to cause economic loss. Results were compared against random at…

构建一个强大的CPS防御系统：一种进化方法

Developing a Strong CPS Defender: An Evolutionary Approach

Evo-Defender通过动态攻击者-防御者互动，提升CPS防御能力，实现更高检测性能。