Science Hackathons for Cyberphysical System Security Research: Putting CPS testbed platforms to good use

A challenge is to develop cyber-physical system scenarios that reflect the diversity and complexity of real-life cyber-physical systems in the research questions that they address. Time-bounded collaborative events, such as hackathons, jams and sprints, are increasingly used as a means of bringing groups of individuals together, in order to explore challenges and develop solutions. This paper describes our experiences, using a science hackathon to bring individual researchers together, in order to develop a common use-case implemented on a shared CPS testbed platform that embodies the diversity in their own security research questions. A qualitative study of the event was conducted, in order to evaluate the success of the process, with a view to improving future similar events

Cybersecurity of Industrial Cyber-Physical Systems: A Review

Industrial cyber-physical systems (ICPSs) manage critical infrastructures by controlling the processes based on the "physics" data gathered by edge sensor networks. Recent innovations in ubiquitous computing and communication technologies have prompted the rapid integration of highly interconnected systems to ICPSs. Hence, the "security by obscurity" principle provided by air-gapping is no longer followed. As the interconnectivity in ICPSs increases, so does the attack surface. Industrial vulnerability assessment reports have shown that a variety of new vulnerabilities have occurred due to this transition while the most common ones are related to weak boundary protection. Although there are existing surveys in this context, very little is mentioned regarding these reports. This paper bridges this gap by defining and reviewing ICPSs from a cybersecurity perspective. In particular, multi-dimensional adaptive attack taxonomy is presented and utilized for evaluating real-life ICPS cyber incidents. We also identify the general shortcomings and highlight the points that cause a gap in existing literature while defining future research directions.Comment: 32 pages, 10 figure

Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems

Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight

Identifying and Detecting Attacks in Industrial Control Systems

The integrity of industrial control systems (ICS) found in utilities, oil and natural gas pipelines, manufacturing plants and transportation is critical to national wellbeing and security. Such systems depend on hundreds of field devices to manage and monitor a physical process. Previously, these devices were specific to ICS but they are now being replaced by general purpose computing technologies and, increasingly, these are being augmented with Internet of Things (IoT) nodes. Whilst there are benefits to this approach in terms of cost and flexibility, it has attracted a wider community of adversaries. These include those with significant domain knowledge, such as those responsible for attacks on Iran’s Nuclear Facilities, a Steel Mill in Germany, and Ukraine’s power grid; however, non specialist attackers are becoming increasingly interested in the physical damage it is possible to cause. At the same time, the approach increases the number and range of vulnerabilities to which ICS are subject; regrettably, conventional techniques for analysing such a large attack space are inadequate, a cause of major national concern. In this thesis we introduce a generalisable approach based on evolutionary multiobjective algorithms to assist in identifying vulnerabilities in complex heterogeneous ICS systems. This is both challenging and an area that is currently lacking research. Our approach has been to review the security of currently deployed ICS systems, and then to make use of an internationally recognised ICS simulation testbed for experiments, assuming that the attacking community largely lack specific ICS knowledge. Using the simulator, we identified vulnerabilities in individual components and then made use of these to generate attacks. A defence against these attacks in the form of novel intrusion detection systems were developed, based on a range of machine learning models. Finally, this was further subject to attacks created using the evolutionary multiobjective algorithms, demonstrating, for the first time, the feasibility of creating sophisticated attacks against a well-protected adversary using automated mechanisms

Digital-twin-based testing for cyber–physical systems: a systematic literature review

Context: Cyber–physical systems present a challenge to testers, bringing complexity and scale to safety-critical and collaborative environments. Digital twins enhance these systems through data-driven and simulation based models coupled to physical systems to provide visualisation, predict future states and communication. Due to the coupling between digital and physical worlds, digital twins provide a new perspective into cyber–physical system testing. Objective: The objectives of this study are to summarise the existing literature on digital-twin-based testing. We aim to uncover emerging areas of adoptions, the testing techniques used in these areas and identify future research areas. Method: We conducted a systematic literature review which answered the following research questions: What cyber–physical systems are digital twins currently being used to test? How are test oracles defined for cyber–physical systems? What is the distribution of white-box, black-box and grey-box modelling techniques used for digital twins in the context of testing? How are test cases defined and how does this affect test inputs? Results: We uncovered 26 relevant studies from 480 produced by searching with a curated search query. These studies showed an adoption of digital-twin-based testing following the introduction of digital twins in industry as well as the increasing accessibility of the technology. The oracles used in testing are the digital twin themselves and therefore rely on both system specification and data derivation. Cyber–physical systems are tested through passive testing techniques, as opposed to either active testing through test cases or predictive testing using digital twin prediction. Conclusions: This review uncovers the existing areas in which digital twins are used to test cyber–physical systems as well as outlining future research areas in the field. We outline how the infancy of digital twins has affected their wide variety of definitions, emerging specialised testing and modelling techniques as well as the current lack of predictive ability

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

Community Stakeholder Perspectives Around the Strengths and Needs of Unaccompanied Immigrant Minors

Unaccompanied immigrant minors (UIM) are youth who lack lawful immigration status and who are without a parent or guardian in the U.S. who can provide custody and care. By all accounts, UIM experience stressful and traumatic circumstances before, during and postmigration. Most UIM left their home countries due to economic stagnation, poverty, crime and gang-related violence (Kandel et al., 2014); almost half described fleeing societal violence and one in five described experiencing domestic abuse (UNHCR, 2014). During migration, UIM are vulnerable to human trafficking, kidnapping, and other abuses (Kandel et al., 2014). Upon resettlement, UIM sometimes experience extended stays in detention centers, community violence exposure in resettlement areas, and an uncertain future in the U.S., all without family support to buffer these stressors (Alvarez & Alegria, 2016). Not surprisingly, UIM are at increased risk for mental health problems compared to accompanied immigrant youth (Derluyn & Broekaert, 2008; Huemer et al., 2009). Research on protective factors is emerging, but scarce.This presentation describes community stakeholder perspectives around the strengths and needs of UIM. Stakeholders include academic researchers with experience working with UIM; key decision makers in agencies serving UIM; professionals with insider knowledge (e.g., immigration attorneys, psychologists with expertise in asylum evaluations); and community members participating in immigrant-focused coalitions. Stakeholder interviews identified significant need for support for UIM. They noted that UIM need emotional support before, during and after legal interviews when youth must recount traumatic events. Families need support during periods of separations and reunions, which can lead to uncertainty and unanticipated conflict, and foster families sponsoring UIM need parenting support for raising children facing difficult circumstances. Stakeholders also noted role conflicts that arise when simultaneously addressing the legal and mental health needs of UIM and the emotional toll that this work takes on professionals serving UIM

Is a Theory of the Problem Sufficient for a Theory of the Solution? Negotiating Tensions among Research, Practice, Advocacy and Activism in Serving Immigrant Communities

The lives of members of immigrant communities are inevitably shaped by U.S. laws, rapidly-shifting immigration policy, institutional policies and practices (e.g., in schools), and how immigrants are welcomed (or not) by members of host communities (Portes & Rumbaut, 2001). These and other aspects of the context of reception have important implications for immigrant integration, education and employment, and mental health. Accordingly, there have been significant calls for psychologists to take active roles in advocacy and activism, which resonates deeply with many of us. Roundtable organizers are community psychologists working with immigrant communities and seeking to negotiate the tensions that can arise at the intersections of research, practice, advocacy and activism. For example: • APA’s Toolkit for Local Advocacy defines advocacy as sharing information within a system with the assumption that the information will help the system respond effectively; activism, on the other hand, is more likely to indict systems perceived as unjust, perhaps from the outside. How does one choose between--or balance--advocacy and activism? What are the advantages and disadvantages of each for trying to solve specific problems in different contexts? • How does one balance social science and research goals that presumably could provide valuable information in working with immigrant communities with advocacy and activism goals? Can we have one without the other, and if so, should we? • If we integrate these roles, do we run the risk of being perceived as less objective on one hand and less invested in communities (or complicit in injustice) on the other? • Is a theory of the problem sufficient for a theory of the solution? Is it possible to move from problems to solutions without the insight and influence that insiders can provide? Participants will share the (imperfect) ways they have balanced research, practice, advocacy and activism in their work