Towards fault tolerance and attack resiliency in cyber-physical energy systems through learning from data streams under harsh learning conditions

现代社会的福利和安全依赖于复杂的安全关键网络物理系统（CPS）的安全和可靠运行。随着现代工业数字化的发展，CPS现在应用于各种技术领域，包括能源，汽车，医药，工业，交通和国防。CPS被定义为物理过程和网络组件的紧密交互和无缝组合。这些网络模块监测、决策和控制物理组件，并使自己适应非静止环境的变化。由于CPS故障发生的可能性很大，攻击面很广，攻击和故障的后果很严重，因此安全和安保是CPS运营的主要问题。CPS对数字化、无线通信和远程控制系统的依赖增加了其对恶意威胁和网络攻击的脆弱性，这导致系统完整性和功能的丧失。这些问题扩大了可能出现的问题的范围，除非对安全和安保特点有统一的看法，否则这些问题就无法得到妥善处理。为了保持CPS的高水平性能、安全性和安全性，必须快速检测异常系统操作和异常，包括故障（安全相关事件）以及恶意威胁和网络攻击（安全相关事件）。虽然网络攻击和故障来自不同的来源，但可能具有相似的特征，并导致运营成本增加，线路关闭的可能性以及对环境造成不利影响的可能性。然而，必须确定每一个问题的来源和严重程度，以便迅速采取纠正措施。因此，早期检测和诊断网络攻击和故障对于公司在世界市场上保持竞争力至关重要。此外，从安全和安保的统一观点来看，将网络攻击与故障进行分类对于评估其对系统完整性的可能影响以及选择一套适当的预防和恢复行动以提高复原力至关重要。此外，许多网络攻击和系统故障不仅会造成安全、安保或危及生命的后果，还可能严重影响生态。为了提高系统的弹性，整合机器学习、大数据分析、控制论、网络安全和安全方面的知识至关重要，以解决潜在的故障和恶意威胁。其目标是关注检测、诊断和预后中缺失的主要知识，沿着机器学习、大数据分析和控制论，共同为攻击弹性和容错CPS铺平道路。虽然拟议的研究可以应用于广泛的应用，但该提案的重点是网络物理能源和电力系统，并应用于现代电网。

The welfare and security of modern societies rely on the safe and secure operation of complex safety-critical cyber-physical systems (CPSs). With advancement of digitalization in modern industries, nowadays, CPSs are applied in various technical areas including energy, automotive, medicine, industries, transportation, and defense. CPSs are defined as the close interaction and seamless combination of physical processes and cyber components. These cyber modules monitor, make decisions and control the physical components, and adapt themselves to changes in non-stationary environments. Safety and security are major concerns for the CPS operation due to the great potential for the occurrence of faults, the broad attack surface, and the severe consequences of attacks and faults. CPSs dependency on digitalization, wireless communication, and remote control systems increases their vulnerabilities to malicious threats and cyber-attacks, which lead to the loss of system integrity and functionality. These widen the range of possible problems that cannot be properly addressed, unless under a unified view of safety and security characteristics. To maintain a high level of performance, safety, and security in CPSs; abnormal system operations and anomalies including faults (safety-related incidents) as well as malicious threats and cyber-attacks (security-related incidents) must be detected quickly. Although cyber-attacks and faults originate from different sources, may have similar signatures, and result in increased operating costs, the chance of line shutdown, and the possibility of detrimental environmental impacts. Nevertheless, the source and severity of each must be identified, so that corrective actions can be taken promptly. Therefore, early detection and diagnosis of cyber-attacks and faults are strategically essential for companies to remain competitive in world markets. In addition, from the unified view of safety and security, classifying cyber-attacks from faults is of paramount importance for assessing their possible effects on the system integrity and choosing an appropriate set of preventive and recovery actions for resilience. Furthermore, many cyber-attacks and system malfunctions do not have only safety, security, or life-threatening consequences but may seriously affect the ecology. To enhance the system resiliency, it is crucial to integrate the knowledge on machine learning, big data analytics, cybernetics, cyber security, and safety, to address potential failures and malicious threats. The objective is to focus on missing principal knowledge in detection, diagnosis, and prognosis along with machine learning, big data analytics, and cybernetics that would pave the way together towards attack-resilient and fault-tolerant CPSs. Although the proposed research can be applied to a wide range of applications, the focus of this proposal is toward cyber-physical energy and power systems, with applications to modern power grids.