Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Development and Analysis of a Model for Assessing Perceived Security Threats and Characteristics of Innovating for Wireless Networks

This dissertation employed a two prong approach, whereby the survey and case study methods were used to investigate security issues regarding wireless networks. The survey portion draws together two previously unrelated research streams. Given the recent increased concern for security in the computing milieu, Innovation Diffusion Theory and security factor constructs were merged and synthesized to form a new instrument. This instrument is useful in an effort to understand what role security concerns play in the adoption and diffusion of technology. In development of the new instrument, 481 usable surveys were collected and analyzed. Factor analysis revealed favorable factor loadings in the data. Further analysis was then conducted utilizing multiple regression analysis. This analysis led to the discovery that the constructs of Susceptibility and Severity of Threat, Improvement Potential, and Visibility are significant predictors in regard to level of concern when using wireless networks. Case studies were conducted with a goal to gain a deep knowledge of IT professionals? concerns, attitudes, and best practices toward wireless security. To this end, seven IT professionals were personally interviewed regarding their perceptions and attitudes toward wireless security. In an effort to compare IT professional and end user opinions, 30 IT professionals also completed a paper based survey regarding their perceptions about security. Findings indicate that security professionals are very optimistic for the future of wireless computing. However, that optimism is tempered by a realization that there are a myriad of potential threats that might exploit weakness in wireless security. To determine differences and similarities between users? perspectives and managers? perspectives regarding wireless network security, the results from the survey and case study were synthesized. Most IT professionals (76.19%) reported that, all factors considered, they prefer to use wired networks as opposed to wireless networks; whereas, substantially fewer (44.86%) of the end user respondents reported that they preferred wired over wireless networks. Overall, results suggest that IT professionals are more concerned about security than are end users. However, a challenge remains to make administrators and users aware of the full effect of security threats present in the wireless computing paradigm

Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Predictive validity of the HCR20v3 within Scottish forensic inpatient facilities: a closer look at key dynamic variables

Introduction: Poor insight is included as a risk factor for violence in risk assessment tools such as the Historical Clinical Risk-Management-20 version 3 (HCR-20v3) yet there is a lack of consensus around the relationship between poor insight and violence in individuals with psychosis. A systematic literature review was therefore carried out to clarify this relationship. Relatedly, a research project aimed to outline the predictive validity of the HCR-20v3 total and sub-scale scores to violence in forensic inpatients. A secondary aim was to understand the predictive ability of 2 dynamic risk factors within the HCR-20v3 clinical sub-scale; insight and positive symptoms, alongside age and history of violence in relation to violence in psychosis. Method: A systematic search of studies investigating insight and violence in patients with psychosis, published between 1980 and 2016 was carried out on relevant databases.17 articles from combined search results of 5694, met the inclusion criteria. These were selected for full-text review and quality grading which was subject to inter-rater reliability. In the research project, the predictive validity of the HCR-20v3 to violence was assessed in N=167 forensic inpatients. A sub-sample of N=135 was then used to investigate insight, positive symptoms, age and history of violence in relation to violence. Data was extracted from case files, with the exception of violence data which was collected prospectively from date of HCR-20v3 publication via DATIX. Results: The systematic review found 8 studies in support of a positive relationship between poor insight and violence, whilst 9 studies did not support this relationship. The majority of better quality studies measured the clinical insight dimension which tended to demonstrate a positive relationship between poor insight and violence. Methodological limitations were apparent across studies. The research project found HCR-20v3 total and clinical and risk-management sub-scale scores to predict violence. The clinical sub-scale was the strongest predictor of violence and physical violence specifically. Sub-sample analysis found positive symptoms and history of violence to significantly predict violence generally whilst only positive symptoms demonstrated prediction of physical violence. Insight and age were not significantly associated with either violence type. Discussion: The systematic review found partial support for a positive relationship between poor insight and violence in psychosis. Future good quality research is required to develop a fuller understanding of this issue. Research project results support the use of the HCR-20v3 in the risk assessment and management of forensic inpatients. They reinforce the usefulness of dynamic risk factors within the clinical sub-scale in particular. In line with the majority of studies within the systematic review however, a relationship between insight and violence in a sub-sample of patients with psychosis was not found. Recommendations are made for the regular re-assessment of dynamic risk factors within the HCR-20v3 clinical sub-scale in order to support patients to reduce their level of risk, with the caveat that future research is still required to support a relationship between insight and violence in this patient group