The growing number of attacks against cyber-physical systems (CPSs) in recent years elevates the concern for cybersecurity of industrial control systems (ICSs). The current efforts of ICS cybersecurity are mainly based on firewalls, data diodes and other methods of intrusion prevention, which may not be sufficient for growing cyber threats from motivated attackers. To enhance the cybersecurity of ICS, a cyber-attack detection system built on the concept of defense-in-depth is developed utilizing network traffic data, host system data, and measured process parameters. This attack detection system provides multiple-layer defense in order to gain the defenders precious time before unrecoverable consequences occur in the physical system. The data used for demonstrating the proposed detection system are from a real-time ICS testbed. Five attacks, including man in the middle (MITM), denial of service (DoS), data exfiltration, data tampering, and false data injection, are carried out to simulate the consequences of cyber-attack and generate data for building data-driven detection models. Four classical classification models based on network data and host system data are studied, including k-nearest neighbor (KNN), decision tree, bootstrap aggregating (Bagging), and random forest, to provide a secondary line of defense of cyber-attack detection in the event that the intrusion prevention layer fails. Intrusion detection results suggest that KNN, Bagging, and random forest have low missed alarm and false alarm rates for MITM and DoS attacks, providing accurate and reliable detection of these cyber-attacks. Cyber-attacks that may not be detectable by monitoring network and host system data, such as command tampering and false data injection attacks by an insider, are monitored for by traditional process monitoring protocols. In the proposed detection system, an auto-associative kernel regression (AAKR) model is studied to strengthen early attack detection. The result shows that this approach detects physically-impactful cyber-attacks before significant consequences occur. The proposed multiple-layer datadriven cyber-attack detection system utilizing network, system, and process data is a promising solution for safeguarding an ICS.

Prognostic algorithms can be divided into three major categories. The most basic methods model the component or system reliability using failure time data and conventional models such as the Weibull. When information pertaining to the operating condition and environmental stressors are available, stress-based techniques can be used. The third type of prognostics is termed effects-based. It is truly an individual based prognostics because it uses information as to how the individual component is affected by the usage condition. This paper presents a summary of the three prognostic types and describes the ongoing development of a Matlab-based set of tools to facilitate prognostic model development. The application of models of each type is illustrated with the PHM Challenge data set. The paper shows the advantages of identifying a degradation parameter to provide for the use of effects-based prognostics.

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions.

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. This class of prognostic algorithms is termed effects-based, or Type III, prognostics. A unit-specific prognostic model, called the General Path Model, involve identifying an appropriate degradation measure to characterize the system's progression to failure. A functional fit of this parameter is then extrapolated to a pre-defined failure threshold to estimate the remaining useful life of the system or component. This paper proposes a specific formulation of the General Path Model with dynamic Bayesian updating as one effects-based prognostic algorithm. The method is illustrated with an application to the prognostics challenge problem posed at PHM '08.