Network and System Management using IEC 62351-7 in IEC 61850 Substations: Design and Implementation

Substations are a prime target for threat agents aiming to disrupt the power grid’s operation. With the advent of the smart grid, the power infrastructure is increasingly being coupled with an Information and Communication Technologies (ICT) infrastructure needed to manage it, exposing it to potential cyberattacks. In order to secure the smart grid, the IEC 62351 specifies how to provide cybersecurity to such an environment. Among its specifications, IEC 62351-7 states to use Network and System Management (NSM) to monitor and manage the operation of power systems. In this research, we aim to design, implement, and study NSM in a digital substation as per the specifications of IEC 62351-7. The substation is one that conforms to the IEC 61850 standard, which defines how to design a substation leveraging ICT. Our contributions are as follows. We contribute to the design and implementation of NSM in a smart grid security co-simulation testbed. We design a methodology to elaborate cyberattacks targeting IEC 61850 substations specifically. We elaborate detection algorithms that leverage the NSM Data Objects (NSM DOs) of IEC 62351- 7 to detect the attacks designed using our method. We validate these experimentally using our testbed. From this work, we can provide an initial assessment of NSM within the context of digital substations

New Directions in Robust Time-Series Machine Learning Theory, Algorithms, and Applications

Despite the rapid progress in research on the robustness of deep neural networks (DNNs) for images and text, there is little principled work for the time-series domain. Since time-series data arises in diverse applications, including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. Safe deployment of time-series DNNs for real-world applications relies on their ability to be resilient against natural/adversarial perturbations and anomalous inputs that may affect their predictive performance. This dissertation studies the design of robust machine learning (ML) algorithms that aim to minimize both the risk and uncertainty of wrongful decisions made by time-series-based ML systems from both theoretical and algorithmic perspectives.First, we investigate the robustness against adversarial time-series inputs. Adversarial examples were shown to be successful in exposing fundamental blind spots in ML models. While adversarial examples expose how to break the models, the process of creating adversarialexamples can itself improve the robustness of ML models by adding them to the training set. The time-series modality poses unique challenges for studying adversarial robustness that are not seen in images and text. The key challenge is how to assess the similarity in the time-series input space to efficiently create valid time-series adversarial examples. Second, we investigate the challenge of Out-of-Distribution (OOD) detection, where the ML system is required to identify time-series inputs that do not follow the distribution of training data. This is a critical task as deep models often make predictions that are very confident yet incorrect on such examples. Detecting OOD examples is challenging, and the potential risks are high for sensitive applications. The key challenge for time-series inputs is how to identify the features that improve the separability between OOD examples and training examples.Motivated by these goals, this dissertation proposes and evaluates a suite of novel solutions to push the frontiers of robust time-series ML: 1) The practical threats of adversarial examples to time-series ML systems; 2) The use of constraints on statistical features of thetime-series data to construct adversarial examples, and providing formal robustness certificates for time-series data; 3) The use of elastic measures such as Dynamic Time Warping to quantify the similarity between time-series examples and developing theoretically-soundalgorithms to efficiently construct valid adversarial examples, and to train robust ML models by explicitly solving a min-max optimization problem; 4) Adapting and applying the developed algorithms to real-world applications including wearable sensors enabled ML systemsfor healthcare to handle both natural perturbations and missing sensor data; and 5) A novel OOD detection algorithm based on deep generative models for the time-series domain and explain why prior OOD methods from the other domains perform poorly

Application Adaptive Bandwidth Management Using Real-Time Network Monitoring.

Application adaptive bandwidth management is a strategy for ensuring secure and reliable network operation in the presence of undesirable applications competing for a network’s crucial bandwidth, covert channels of communication via non-standard traffic on well-known ports, and coordinated Denial of Service attacks. The study undertaken here explored the classification, analysis and management of the network traffic on the basis of ports and protocols used, type of applications, traffic direction and flow rates on the East Tennessee State University’s campus-wide network. Bandwidth measurements over a nine-month period indicated bandwidth abuse of less than 0.0001% of total network bandwidth. The conclusion suggests the use of the defense-in-depth approach in conjunction with the KHYATI (Knowledge, Host hardening, Yauld monitoring, Analysis, Tools and Implementation) paradigm to ensure effective information assurance

Fluid aggregations for Markovian process algebra

Quantitative analysis by means of discrete-state stochastic processes is hindered by the well-known phenomenon of state-space explosion, whereby the size of the state space may have an exponential growth with the number of objects in the model. When the stochastic process underlies a Markovian process algebra model, this problem may be alleviated by suitable notions of behavioural equivalence that induce lumping at the underlying continuous-time Markov chain, establishing an exact relation between a potentially much smaller aggregated chain and the original one. However, in the modelling of massively distributed computer systems, even aggregated chains may be still too large for efficient numerical analysis. Recently this problem has been addressed by fluid techniques, where the Markov chain is approximated by a system of ordinary differential equations (ODEs) whose size does not depend on the number of the objects in the model. The technique has been primarily applied in the case of massively replicated sequential processes with small local state space sizes. This thesis devises two different approaches that broaden the scope of applicability of efficient fluid approximations. Fluid lumpability applies in the case where objects are composites of simple objects, and aggregates the potentially massive, naively constructed ODE system into one whose size is independent from the number of composites in the model. Similarly to quasi and near lumpability, we introduce approximate fluid lumpability that covers ODE systems which can be aggregated after a small perturbation in the parameters. The technique of spatial aggregation, instead, applies to models whose objects perform a random walk on a two-dimensional lattice. Specifically, it is shown that the underlying ODE system, whose size is proportional to the number of the regions, converges to a system of partial differential equations of constant size as the number of regions goes to infinity. This allows for an efficient analysis of large-scale mobile models in continuous space like ad hoc networks and multi-agent systems

A Heterogeneous Communications Network for Smart Grid by Using the Cost Functions

Smart Grids (SG) is an intelligent power grid in which the different SG node types with different communication requirements communicates different types of information with Control Stations (CS). Radio Access Technologies (RATs) due to its advantages are considered as the main access method to be used in order to have bidirectional data transferring between different node types and CS. Besides, spectrum is a rare source and its demand is increasing significantly. Elaborating a heterogeneous in order to fulfill different SG node types communication requirements effectively, is a challenging issue. To find a method to define desirability value of different RAT to support certain node types based on fitness degree between RAT communication characteristics and node type communication requirements is an appropriate solution. This method is implemented by using a comprehensive Cost Function (CF) including a communication CF (CCF) in combination with Energy CF (ECF). The Key Point Indicators which are used in the CCF are SG node type communication requirements. The existing trade of between Eb/N0 and spectral efficiency is considered as ECF. Based on the achieved CCF and ECF and their tradeoffs, SG node types are assigned to different RATs. The proposed assigning method is sensitive to the SG node types densities. The numerical results are achieved by using MATLAB simulation. The other different outcomes of the research output such as cognitive radio in SG and collectors effect number on data aggregation are discussed as well

Fluid aggregations for Markovian process algebra

Quantitative analysis by means of discrete-state stochastic processes is hindered by the well-known phenomenon of state-space explosion, whereby the size of the state space may have an exponential growth with the number of objects in the model. When the stochastic process underlies a Markovian process algebra model, this problem may be alleviated by suitable notions of behavioural equivalence that induce lumping at the underlying continuous-time Markov chain, establishing an exact relation between a potentially much smaller aggregated chain and the original one. However, in the modelling of massively distributed computer systems, even aggregated chains may be still too large for efficient numerical analysis. Recently this problem has been addressed by fluid techniques, where the Markov chain is approximated by a system of ordinary differential equations (ODEs) whose size does not depend on the number of the objects in the model. The technique has been primarily applied in the case of massively replicated sequential processes with small local state space sizes. This thesis devises two different approaches that broaden the scope of applicability of efficient fluid approximations. Fluid lumpability applies in the case where objects are composites of simple objects, and aggregates the potentially massive, naively constructed ODE system into one whose size is independent from the number of composites in the model. Similarly to quasi and near lumpability, we introduce approximate fluid lumpability that covers ODE systems which can be aggregated after a small perturbation in the parameters. The technique of spatial aggregation, instead, applies to models whose objects perform a random walk on a two-dimensional lattice. Specifically, it is shown that the underlying ODE system, whose size is proportional to the number of the regions, converges to a system of partial differential equations of constant size as the number of regions goes to infinity. This allows for an efficient analysis of large-scale mobile models in continuous space like ad hoc networks and multi-agent systems

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Evaluation and prediction of agonistic behaviour in the domestic dog

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination