Due to perceptions of efficiency and significant productivity gains, various organisations, including in education, are adopting Large Language Models (LLMs) into their workflows. Educator-facing, learner-facing, and institution-facing LLMs, collectively, Educational Large Language Models (eLLMs), complement and enhance the effectiveness of teaching, learning, and academic operations. However, their integration into an educational setting raises significant cybersecurity concerns. A comprehensive landscape of contemporary attacks on LLMs and their impact on the educational environment is missing. This study presents a generalised taxonomy of fifty attacks on LLMs, which are categorized as attacks targeting either models or their infrastructure. The severity of these attacks is evaluated in the educational sector using the DREAD risk assessment framework. Our risk assessment indicates that token smuggling, adversarial prompts, direct injection, and multi-step jailbreak are critical attacks on eLLMs. The proposed taxonomy, its application in the educational environment, and our risk assessment will help academic and industrial practitioners to build resilient solutions that protect learners and institutions.

Distribution grids are becoming more decentralized with the increasing penetration of renewable energy resources such as storages, electrical vehicles and renewable energy resources. IEC 61850 is the dominant standard for engineering substation automation systems and the scope is traditionally within the Local Area Network. The scope has since extended to include wide area networks and cybersecurity is now a system requirement that is becoming a must-have. We propose EASy-RM, a requirement management framework for Energy Automation Systems that can trace cybersecurity requirements from requirements (IEC 62443) to specifications (IEC 61850) and to the automation control (IEC 61499). The underlying framework is based on graph theory for formally linking requirements and system artefacts, and graph algorithms over these links are used for requirement management activities. Our results are demonstrated on a CIGRE case study where we demonstrate the tracing of IEC 62443 authentication requirements to IEC 61850 specifications and IEC 61499 implementation.

Industrial Cyber-Physical Systems (ICPS) are heterogeneous computer systems interacting with physical processes in an industrial environment. The presence of numerous interconnected components poses significant security threats to ICPS. Slow-Rate Attacks (SRA), in which attackers attack a system constantly at low volumes, are difficult to detect for resource-constrained ICPS computers like programmable logic controllers (PLC). We propose an optimised light-weight active security framework for SRA detection based on Online Sequential Extreme Learning Machine (OSELM). We optimise the memory and space footprint of OSELM for deployment in resource-constrained ICPS. Additionally, a simple stratified k-fold cross training method improves the performance and accuracy of binary and multi-class SRA detection. Compared to existing methods, our technique requires less space and reduces attack detection time by at least 95%.

Effectively caring for adults with depression is challenging. While technology offers potential improvements in emotional well-being through better monitoring, standardised methods to gather and analyse relevant data are highly fragmented. This Systematic Literature Review (SLR) explores using Internet of Things (IoT) based data collection and analysis to enhance emotional well-being and manage depression effectively. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we report in-depth findings from 42 studies, which were selected from an initial set of 559 published works. We find that current literature extensively covers important topics like IoT for detecting, analysing, and monitoring emotions, therapeutic interventions for emotional well-being, and predicting, detecting, and managing depression. IoT-based data collection and analysis solutions predominantly employ sensors and AI, respectively. The literature review identifies a gap in prioritising active systems that engage users, highlighting the need to address key aspects such as privacy and security.

This paper examines the use of supervised machine learning to construct a digital twin model replicating a physical plant. An inverted pendulum simulation has been used as a case study.A comparative study was conducted on single-step and multistep Dense models, Convolutional Neural Network (CNN), Recurrent Neural Net (RNN), and a Residual Neural Net (RNN2) models to investigate the most appropriate model for replicating the plant model.The study found that single-step models were consistently more accurate than multi-step models due to single-step model’s iterative nature. Whereas, multi-step models were better at revealing prediction patterns and identifying causes for large deviations. The best-performing model was the RNN2 model, however, signs of overfitting were observed. In general, all models were able to take into account minor random actuation, however, large changes such as the pendulum falling over caused the models to behave sporadically.

In smaller components of industrial or energy automation systems, such as device controllers of Protection and Control (PAC) systems in smart grids, controller functionality is tightly coupled with the physical device or sensor capabilities. At this level, software is small and therefore easy to maintain and test. However, when multiple controllers are interconnected and higher-level functionality is added, software applications grow exponentially, and ensuring maintainability becomes proportionally challenging. In this paper, we extend the IEC 61499 reference architecture used to develop industrial automation software with the principles of Abstraction Layered Architecture (ALA) that has shown up to 400% improvements in industrial software maintainability. We show that even a light application of abstraction layering on the top, application-level of IEC 61499 applications makes them significantly more readable and slightly more maintainable. More concrete gains in maintainability are expected when abstraction layering is integrated into lower layers.

The significant growth in modern communication technologies has led to an increase in zero-day vulnerabilities that degrade the performance ofindustrialcyber-physical systems (ICPS). Distributed denial of service (DDoS) attacks are one such threat that overwhelms a target with floods of packets, posing a severe risk to the normal operations of the ICPS. Current solutions to detect DDoS attacks are unsuitable for resource-constrained ICPS. This study proposes actively detecting multiscale flooding DDoS attacks in resource-constrained ICPS by analyzing network traffic in the frequency domain. A two-phased technique detects attack presence and attack volume. Both phases use a novel combination of light-weight and theoretically sound statistical methods. The effectiveness of the proposed technique is evaluated using mainstream metrics like true and false positive rates, accuracy, and precision using BOUN DDoS 2020 and CICDDoS 2019 datasets. An implementation of the proposed approach on a programmable logic controllers-based ICPS demonstrated improvements in resource usage and detection time compared to the existing state-of-the-art.

Quantification research has sought to accurately estimate class distributions under dataset shift. While existing methods perform well under assumed conditions of shift, it is not always clear whether such assumptions will hold in a given application. This work extends the analysis and experimental evaluation of our Gain-Some-Lose-Some (GSLS) model for quantification under general dataset shift and incorporates it into a method for dynamically selecting the most appropriate quantification method. Selection by a Kolmogorov-Smirnov test for any shift followed by a newly proposed “Adjusted Kolmogorov-Smirnov” test for non-prior shift is found to best balance quantification and runtime performance. We also present a framework for constraining quantification prediction intervals to user-specified limits by requesting a smaller set of instance class labels from the user than required with confidence-based rejection.

Abstract We must explicitly capture relationships and hierarchies between the multitude of system and security standards requirements. Current security requirements specification methods do not capture such structure effectively, making requirements management and traceability harder, consequently increasing costs and time to market for developing certified ICS. We propose a novel requirements repository model for ICS that uses labelled property graphs to structure and store system-specific and standards-based requirements using well-defined relationship types. Furthermore, we integrate the proposed requirements repository with design-time ICS tools to establish requirements traceability. A wind turbine case study illustrates the overall workflow in our framework. We demonstrate that a robust requirements traceability matrix is a natural consequence of using labelled property graphs. We also introduce a compatible requirements change management procedure that aids in adapting to changes in development and certification schemes.

Unknown and unplanned downtime events during production cause significant disruption and loss of productivity. Investigating, identifying and addressing such events is a pressing need. The primary objective of this study is to examine downtime, performance loss, and quality control in the manufacturing process. Specifically, we propose a solution that provides real-time data processing and visualization of the factory floor. This solution was implemented for a major food manufacturer based in New Zealand. The company provided historical data covering over six years of operation and access to real-time data through their Industrial Internet of Things (IIoT) systems executing on Programmable Logic Controllers (PLCs). Our solution is an Overall Equipment Effectiveness (OEE) standardized Supervisory Control and Data Acquisition (SCADA) system that visualizes the manufacturing process in real-time. Analysis of the data collected during this research shows that by implementing the OEE and employing shift adjustment, there was a significant increase in production output. OEE can help improve manufacturing performance by pinpointing the root of the loss of performance in all areas monitored.

Denial of Service is a significant availability threat in Industrial Cyber-Physical systems and smart manufacturing is not an exception. The types, methods, and duration of these attacks have been evolving rapidly and their number has increased dramatically, reaching a new record in history. In particular, digitisation of the manufacturing process and increased connectivity have created a battleground between product quality of service and threats associated with cross-domains and multi-vector attacks that affect the manufacturing system performance. The existing research on cyber-threats related to smart manufacturing system does not consider the comprehensive landscape of denial of service attacks. In this study, we classify well-accepted (distributed) denial of service attacks according to a proposed taxonomy, focusing on both the multi-vector attacks and cross-domain attacks. Utilising the taxonomy, more than fifty different denial of service attacks on smart manufacturing system were classified in terms of Endpoint and Network (distributed) denial of service attacks. As an example, a Cyber-Physical Conveyor System was used to examine the proposed taxonomy.

Code smell detection and refactoring are crucial to sustain quality, reduce complexity and increase the efficiency of a software application. Code smells are observable patterns in the source code of a program that indicate deeper structural issues. Most traditional methods for code smell classification rely exclusively on structural object-oriented metrics and manually-designed heuristics. We propose a novel multimodal deep learning approach that combines structural and semantic information to detect two commonly-encountered code smells: Long Parameter Lists and Switch Statements. The presented architecture applies transfer learning on DistilBERT to generate vector embeddings representing classes and methods concatenated with numerical metrics for joint feature extraction using CNN, to build a complex mapping between the features and predict the output as smelly or non-smelly. Subsequently, to perform a holistic comparative analysis we also implement two multimodal machine learning pipelines, the first employs a sci-kit learn TF-IDF Vectorizer with Random Forest Classifier, and the second merges CNN with Bi-LSTM. Our approach achieves an accuracy of 91.2% as corroborated by experimental evaluation, outperforming the state-of-the-art techniques.

Abstract Contemporary labeled property graph databases are either schema-less or schema-optional to support frequent changes in the structure of data found in domains requiring high flexibility. However, the lack of structure impacts data transformation and loading operations from heterogeneous sources into graph databases. We present a formal algebra for specifying and generating graph schema for labeled property graph databases. We formally define and demonstrate the use of generated graph schemas to systematically transform and load data-sets related to domains of cyber-physical systems, big data analytics and tourism. Findings from three disparate case studies show that -generated schemas assist in enforcing integrity constraints that reduce the chance of data corruption, hence assuring data consistency and integrity.

Effective supervised training of modern machine learning models often requires large labelled training datasets, which could be prohibitively costly to acquire for many practical applications. Research addressing this problem has sought ways to leverage weak supervision sources, such as the user-defined heuristic labelling functions used in the data programming paradigm, which are cheaper and easier to acquire. Automatic generation of these functions can make data programming even more efficient and effective. However, existing approaches rely on initial supervision in the form of small labelled datasets or interactive user feedback. In this paper, we propose Witan, an algorithm for generating labelling functions without any initial supervision. This flexibility affords many interaction modes, including unsupervised dataset exploration before the user even defines a set of classes. Experiments in binary and multi-class classification demonstrate the efficiency and classification accuracy of Witan compared to alternative labelling approaches.

Increased software maintainability can help improve a company's profitability by directly reducing ongoing software development costs. Abstraction Layered Architecture (ALA) is a reference architecture for building maintainable applications, but its effectiveness in commercial projects has remained unexplored. This research, carried out as a 16-month joint industry-academic project, explores developing commercial code bases using ALA and the extent to which ALA improves maintainability. An existing application from Datamars, New Zealand, was re-developed by using ALA and compared with the original application. In order to carry out these comparisons, we developed suitable measures by adapting maintainability characteristics from the ISO 25010 family of standards. Specifically, we determined metrics to capture the five sub-characteristics of maintainability: modularity, reusability, analysability, modifiability, and testability; and used them to test our hypothesis that the use of ALA improved maintainability of the application. During the evaluation, we found that the modularity, reusability, analysability, and testability of the re-developed ALA application were higher than for the original application. The modifiability of the ALA-based application was lower in the short-term, but shown to trend upwards in the longer term. Our findings led to proposing a generalised ALA-based development method that promises a significant reduction in maintenance costs.

When applying supervised learning to estimate class distributions of unlabelled samples (so-called quantification), dataset shift is an expected yet challenging problem. Existing quantification methods make strong assumptions on the nature of dataset shift that often will not hold in practice. We propose a novel Gain-Some-Lose-Some (GSLS) model that accounts for more general conditions of dataset shift. We present a method for fitting the GSLS model without any labelled instances from the target sample, and experimentally demonstrate that GSLS can produce reliable quantification prediction intervals under broader conditions of shift than existing quantification methods.

In Industrial Cyber-Physical Systems (ICPS), containerisation promises high scalability, reconfigurability and dependability. Denial of Service (DoD/DDoS) is a significant security threat in containerised ICPS applications, which execute on resource-constrained computers like PLCs, and cannot support traditional security mechanisms like firewalls that sacrifice performance and throughput. We propose a novel, light-weight active security approach to detecting DoS/DDoS attacks through frequency analysis of network traffic (packets). Our approach identifies attacks by recording a frequency signature of the flow of packets in an ICPS under normal operation. Subsequently, an attack is modelled as any anomalies in the network that modify the frequency profile of network traffic in the ICPS. Our prototype implementation and evaluation show that this active security method is light-weight and suitable for resource-constrained ICPS platforms.

Data scientists, like software engineers, use search engines, code repositories, tutorials, and question and answer sites for finding code snippets. The objective of this study is to understand what information can be extracted from data science experiment repositories for quicker availability of relevant information when data scientists search for information. In this paper, we investigated a set of notebooks to identify recurring data science techniques for efficient information retrieval and easy adaptation from online solutions to support their search during experimentation. From the manual annotation of 57 natural language processing notebooks, a taxonomy on 106 data science techniques was developed, grouped by data science workflow stages. The preliminary evaluation shows that our constructed taxonomy is relevant to retrieve information that data scientists are searching for. Future work will continue to investigate the creation of a context aware code snippet engine designed for data scientists.

Deep learning and computer vision have achieved remarkable success in many areas of machine learning and medical diagnostics. However, there is still a remarkable gap between dermatologists' skin cancer diagnosis and reliable computer-aided melanoma detection. There are several reasons behind this gap, and the availability of insufficient data for training deep learning networks is one of them. Data augmen-tation is a popular technique to increase training data manifolds to mitigate the lack of data. In this paper, a conditional generative adversarial network (CGAN) is proposed to produce high-resolution synthetic images to augment the training data and gain higher performance of skin cancer detection systems. The artificial generation of images resembling real images is a difficult task owing to unstable information present in the skin lesions such as irregular borders, diameter, shape, color, and texture. The generator module of CGAN is designed to aggregate the information from all feature layers and produce synthetic images. Additionally, the generator incorporates the auxiliary information along with image inputs to map latent feature components successfully. The network is trained on 10,015 skin cancer images taken from the International Skin Imaging Collaboration (ISIC 2018). It was concluded from the experiments that the proposed model obtained better classi-fication performance as compared to the imbalanced original dataset and other state-of-the-art methods.

Increasing automation and external connectivity in industrial control systems (ICS) demand a greater emphasis on software-level communication security. In this article, we propose a secure-by-design development method for building ICS applications, where requirements from security standards like ISA/IEC 62443 are fulfilled by design-time abstractions calledsecure links. Proposed as an extension to the IEC 61499 development standard, secure links incorporate both light-weight and traditional security mechanisms into applications with negligible effort. Applications containing secure links can be automatically compiled into fully IEC 61499-compliant software. Experimental results show secure links significantly reduce design and code complexity and improve application maintainability and requirements traceability.

We propose a tool-supported framework to reason about requirements constraining resource distributions and devise strategies for routing essential services in a disaster-affected region. At the core of our approach is the Route Advisor for Disaster-Affected Regions (RADAR) framework that operates on high-level algebraic representations of the region, modelled as a cyber-physical system (cps) where resource distribution is carried out over an infrastructure connecting physical geographical locations. The Satisfiable-Modulo Theories (SMT) and graph-theoretic algorithms used by the framework supports disaster management decision-making during response and preparedness phases. We demonstrate our approach on a case study in disaster management and describe scenarios to illustrate the usefulness of RADAR.

We have come to rely on industrial-scale cyber-physical systems more and more to manage tasks and machinery in safety-critical situations. Efficient, reliable fault identification and management has become a critical factor in the design of these increasingly sophisticated and complex devices.Teams of co-operating software agents are one way to co-ordinate the flow of diagnostic information gathered during fault-finding. By wielding domain knowledge of the software architecture used to construct the system, agents build and refine their beliefs about the location and root cause of faults.This paper examines how agents constructed within the GORITE Multi-Agent Framework create and refine their beliefs. We demonstrate three different belief structures implemented within our Fault Diagnostic Engine, showing how each supports a distinct aspect of the agent's reasoning. Using domain knowledge of the IEC 61499 Function Block architecture, agents are able to examine and rigorously evaluate both individual components and entire sub-systems.

Integrating the design and creation of fault identification and diagnostic capabilities into Model-Driven Development methodologies is one approach to enhancing the resilience of Industrial Cyber-Physical Systems. We present a Fault Diagnostic Engine designed to recognise and diagnose faults in IEC 61499 Function Block Applications. Using diagnostic agents that interact directly with the target application, we demonstrate fault monitoring and analysis tech-niques and as well as failure scenario intervention. By designing and building fault diagnostic resources during early phases of Model-Driven Development, both iterative testing and long-term fault management capabilities can be created. While applying and refining appropriate model artifacts, we demonstrate that the concurrent development of function blocks alongside fault management capabilities is both feasible and worthwhile.

Neural saturation in Deep Neural Networks (DNNs) has been studied extensively, but remains relatively unexplored in Convolutional Neural Networks (CNNs). Understanding and alleviating the effects of convolutional kernel saturation is critical for enhancing CNN models classification accuracies. In this paper, we analyze the effect of convolutional kernel saturation in CNNs and propose a simple data augmentation technique to mitigate saturation and increase classification accuracy, by supplementing negative images to the training dataset. We hypothesize that greater semantic feature information can be extracted using negative images since they have the same structural information as standard images but differ in their data representations. Varied data representations decrease the probability of kernel saturation and thus increase the effectiveness of kernel weight updates. The two datasets selected to evaluate our hypothesis were CIFAR-10 and STL-10 as they have similar image classes but differ in image resolutions thus making for a better understanding of the saturation phenomenon. MNIST dataset was used to highlight the ineffectiveness of the technique for linearly separable data. The ResNet CNN architecture was chosen since the skip connections in the network ensure the most important features contributing the most to classification accuracy are retained. Our results show that CNNs are indeed susceptible to convolutional kernel saturation and that supplementing negative images to the training dataset can offer a statistically significant increase in classification accuracies when compared against models trained on the original datasets. Our results present accuracy increases of 6.98% and 3.16% on the STL-10 and CIFAR-10 datasets respectively.

Convolutional Neural Networks (CNNs) specialize in feature extraction rather than function mapping. In doing so they form complex internal hierarchical feature representations, the complexity of which gradually increases with a corresponding increment in neural network depth. In this paper, we examine the feature extraction capabilities of CNNs using Maximum Entropy (ME) and Signal-to-Noise Ratio (SNR) to validate the idea that, CNN models should be tailored for a given task and complexity of the input data. SNR and ME measures are used as they can accurately determine in the input dataset, the relative amount of signal information to the random noise and the maximum amount of information respectively.We use two well known benchmarking datasets, MNIST and CIFAR-10 to examine the information extraction and abstraction capabilities of CNNs. Through our experiments, we examine convolutional feature extraction and abstraction capabilities in CNNs and show that the classification accuracy or performance of CNNs is greatly dependent on the amount, complexity and quality of the signal information present in the input data. Furthermore, we show the effect of information overflow and underflow on CNN classification accuracies. Our hypothesis is that the feature extraction and abstraction capabilities of convolutional layers are limited and therefore, CNN models should be tailored to the input data by using appropriately sized CNNs based on the SNR and ME measures of the input dataset.

Industrial standards for developing medical device software provide requirements that conforming devices must meet. A number of reference software architectures have been proposed to develop such software. The ISO/IEC 25010:2011 family of standards provides a comprehensive software product quality model, including characteristics that are highly desirable in medical devices. Furthermore, frameworks like 4+1 Views provide a robust framework to develop the software architecture or high level design for any software, including for medical devices. However, the alignment between industrial standards and reference architectures for medical device software, on one hand, and ISO/IEC 25010:2011 and 4+1 Views, on the other, is not well understood. This paper aims to explore how ISO/IEC 25010:2011 and 4+1 Views are supported by current standards, namely ISO 13485:2016, ISO 14971:2012, IEC 62304:2006 and IEC 62366:2015, and current reference architectures for medical device software. We classified requirements from medical devices standards into qualities from ISO/IEC 25010:2011 and architectural views from 4+1 Views. A systematic literature review (SLR) method was followed to review current references software architectures and a mapping of their support for the identified ISO/IEC 25010:2011 qualities in the previous step was carried out. Our results show that ISO/IEC 25010:2011 qualities like functional suitability, portability, maintainability, usability, security, reliability and compatibility are highly emphasised in medical device standards. Furthermore, we show that current reference architectures only partially support these qualities. This paper can help medical device developers identify focus areas for developing standards-compliant software. A wider study involving under-development medical devices can help improve the accuracy of our findings in the future.

Cooperative vehicular technology in recent times has aided in realizing some state-of-art technologies like autonomous driving. Effective and efficient prioritization of emergency vehicles (EVs) using cooperative vehicular technology can undoubtedly aid in saving property and lives. Contemporary EV prioritization, called preemption, is highly dependent on existing traffic infrastructure. Accessing crucial decision parameters for preemption like speed, position and acceleration data in real-time is almost impossible in current systems. The connected vehicle can provide such data in real-time, which makes EV preemption more responsive and effective. Also, autonomous vehicles can help in optimizing the timing in traffic phases and minimize human-related loss like higher headway times and inconsistent inter-vehicle spacing when following each other. In this paper, we introduce self-coordinating a decentralized traffic control system termed as Virtual Traffic Light plus for Emergency Vehicle (VTL+EV) to prioritize EVs in an intersection. The proposed system can expedite EVs movement through intersections and impose minimal waiting time for ordinary vehicles. The VTL+EV algorithm also can improve overall throughput making an intersection more efficient.

Background: Testing IoT software is challenging due to large scale, volume of data and heterogeneity. Testing automation is a much-needed feature in the domain.A ims: The first goal of this research is to explore the requirements and challenges of IoT testing automation. The second goal is to integrate testing automation tools used in commercial software into the IoT context. Method: A systematic literature review is carried out to elicit requirements for testing automation in IoT. A design science approach is followed to build a testing automation tool for IoT applications written in the Node-Red platform, using the commercial testing automation tool Selenium. The resulting framework uses the Selenium Web Driver for browser-based testing automation for IoT applications. Results: The proposed framework has been functionally tested on multiple browsers with preliminary evaluation on maintainability, browser capability and comprehensiveness. Conclusions: The use of commercial tools for testing automation in IoT is feasible. However, major challenges like high data volumes and parallel transmission and processing of data need to be addressed comprehensively for complete integration.

As Industrial Cyber–Physical Systems (ICPS) become more connected and widely-distributed, often operating in safety-critical environments, we require innovative approaches to detect and diagnose the faults that occur in them. We profile fault identification and diagnosis techniques employed in the aerospace, automotive, and industrial control domains. Each of these sectors has adopted particular methods to meet their differing diagnostic needs. By examining both theoretical presentations as well as case studies from production environments, we present a profile of the current approaches being employed and identify gaps. A scoping study was used to identify and compare fault detection and diagnosis methodologies that are presented in the current literature. We created categories for the different diagnostic approaches via a pilot study and present an analysis of the trends that emerged. We then compared the maturity of these approaches by adapting and using the NASA Technology Readiness Level (TRL) scale. Fault identification and analysis studies from 127 papers published from 2004 to 2019 reveal a wide diversity of promising techniques, both emerging and in-use. These range from traditional Physics-based Models to Data-Driven Artificial Intelligence (AI) and Knowledge-Based approaches. Hybrid techniques that blend aspects of these three broad categories were also encountered. Predictive diagnostics or prognostics featured prominently across all sectors, along with discussions of techniques including Fault trees, Petri nets and Markov approaches. We also profile some of the techniques that have reached the highest Technology Readiness Levels, showing how those methods are being applied in real-world environments beyond the laboratory. Our results suggest that the continuing wide use of both Model-Based and Data-Driven AI techniques across all domains, especially when they are used together in hybrid configuration, reflects the complexity of the current ICPS application space. While creating sufficiently-complete models is labor intensive, Model-free AI techniques were evidenced as a viable way of addressing aspects of this challenge, demonstrating the increasing sophistication of current machine learning systems. Connecting ICPS together to share sufficient telemetry to diagnose and manage faults is difficult when the physical environment places demands on ICPS. Despite these challenges, the most mature papers present robust fault diagnosis and analysis techniques which have moved beyond the laboratory and are proving valuable in real-world environments.

Graph databases provide better support for highly interconnected datasets than relational databases. However, labeled property graph databases, which have become increasingly popular, are schema-optional, making them prone to data corruption, especially when new users switch from relational databases to graph databases. In this work, we provide a schema-driven formalism for graph databases. This formalism enables schema-driven loading of graph databases from other sources, such as relational databases. Also, this formalism enables schema-driven data analytics that allows for a more structured analysis of data stored in graph databases. Such analytics are based on a boilerplate approach allowing users who are not experts in the use of graph database query languages to carry out analytics efficiently. We showcase the utility of the proposed formalism by considering a case study from Airbnb for illustrating schema-based loading procedures. The proposed schema-driven analytics process is illustrated using another case study from an industrial cyber-physical systems standard. Overall, the schema-driven formalism provides several useful features, such as preventing both data corruption and long-term degradation of graph database structures.

Emergency service vehicles like ambulance, fire, police etc. should respond to emergencies on time. Existing barriers like increased congestion, multiple signalized intersections, queued vehicles, traffic phase timing etc. can prevent emergency vehicles (EVs) achieving desired response times. Existing solutions to route EVs have not been successful because they do not use dynamic traffic parameters. Real time information on increased congestion, halts on road, pedestrian flow, queued vehicles, real and adaptive speed, can be used to properly actuate pre-emption and minimise the impact that EV movement can have on other traffic.Smart cities provide the necessary infrastructure to enable two critical factors in EV routing: real-time traffic data and connectivity. In addition, using autonomous vehicles (AVs) in place of normal emergency service vehicles can have further advantages in terms of safety and adaptability in smart city environments. AVs feature several sensors and connectivity that can help them make real-time decisions. We propose a novel idea of using autonomous emergency vehicles (AEVs) that can meet the critical response time and drive through a complex road network in smart cities efficiently and safely. This is achieved by considering traffic network analogous to real-time systems (RTS) where we use mixed-criticality real-time system (MCRTS) task scheduling to schedule AEVs for meeting response time.

The benefits that arise from the adoption of a systems engineering approach to the design of engineered systems are well understood and documented. However, with software systems, different approaches are required given the changeability of requirements and the malleability of software. With the design of industrial cyber-physical systems, one is confronted with the challenge of designing engineered systems that have a significant software component. Furthermore, that software component must be able to seamlessly interact with both the enterprise's business systems and industrial systems. In this paper, we present Janus, which together with the GORITE BDI agent framework, provides a methodology for the design of agent-based industrial cyber-physical systems. Central to the Janus approach is the development of a logical architecture as in traditional systems engineering and then the allocation of the logical requirements to a BDI (Belief Desire Intention) agent architecture which is derived from the physical architecture for the system. Janus has its origins in product manufacturing; in this paper, we apply it to the problem of Fault Location, Isolation and Service Restoration (FLISR) for power substations.

The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, "Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.

Programmable Logic Controllers (PLCs) execute critical control software that drives Industrial Automation and Control Systems (IACS). PLCs can become easy targets for cyber-adversaries as they are resource-constrained and are usually built using legacy, less-capable security measures. Security attacks can significantly affect system availability, which is an essential requirement for IACS. We propose a method to make PLC applications more security-aware. Based on the well-known IEC 61499 function blocks standard for developing IACS software, our method allows designers to annotate critical parts of an application during design time. On deployment, these parts of the application are automatically secured using appropriate security mechanisms to detect and prevent attacks. We present a summary of availability attacks on distributed IACS applications that can be mitigated by our proposed method. Security mechanisms are achieved using IEC 61499 Service-Interface Function Blocks (SIFBs) embedding Intrusion Detection and Prevention System (IDPS), added to the application at compile time. This method is more amenable to providing active security protection from attacks on previously unknown (zero-day) vulnerabilities. We test our solution on an IEC 61499 application executing on Wago PFC200 PLCs. Experiments show that we can successfully log and prevent attacks at the application level as well as help the application to gracefully degrade into safe mode, subsequently improving availability.

Cyber-Physical Systems (CPS) contain intertwined and distributed software, hardware, and physical components to control complex physical processes. They find wide application in industrial systems, such as smart grid protection systems, which face increasingly complex communication and computation needs. Due to the scale and complexity of the interactions that occur within CPS, tracing requirements through to the system components and software code that implement them is often hard. Existing requirements management systems do not scale well, and traceability is difficult to implement and maintain in highly heterogeneous systems. However, the information trace that links provide is crucial for supporting testing and certification activities in safety-critical environments such as smart grids. The well-formed models of power systems provided by the IEC 61850 standard and the software design structure provided by the IEC 61499 Function Blocks standard can be leveraged to automate many traceability operations. We present Traceability of Requirements Using Splices (TORUS), a novel traceability framework for the development of large-scale safety-critical CPS. TORUS introduces splices , autonomous graph-based data structures that automatically create and manage trace links between requirements and components through the inevitable changes that occur during system development. The formal, graph-based structure of TORUS lends itself well to the development of sophisticated algorithms to automate the extraction of useful traceability information such as historical records and metrics for requirements coverage and component coupling. By capturing not only the current state of the system but also historical information, TORUS allows project teams to see a much richer view of the system and its artifacts. We apply TORUS to the development of a protection system for smart grid substations. In addition, through a number of experiments in splice creation, modification, and application of automated algorithms, we show that TORUS scales easily to large systems containing hundreds of thousands of requirements and system components and millions of possible trace links.

Industrial automation systems (IAS) need to be highly dependable; they should not merely function as expected but also do so in a reliable, safe, and secure manner. Formal methods are mathematical techniques that can greatly aid in developing dependable systems and can be used across all phases of the system development life cycle (SDLC), including requirements engineering, system design and implementation, verification and validation (testing), maintenance, and even documentation. This state-of-the-art survey reports existing formal approaches for creating more dependable IAS, focusing on static formal methods that are used before a system is completely implemented. We categorize surveyed works based on the phases of the SDLC, allowing us to identify research gaps and promising future directions for each phase.

The integration of industrial automation systems and software agents has been practiced for many years. However, such an integration is usually done by experts and there is no consistent way to assess these practices and to optimally select one for a specific system. Standards such as the ISO/IEC 25023 propose measures that could be used to obtain a quantification on the characteristics of such integration. In this work, the suitability of these characteristics and their proposed calculation for assessing the connection of industrial automation systems with software agents is discussed. Results show that although most of the measures are relevant for the integration of agents and industrial automation systems, some are not relevant in this context. Additionally, it was noticed that some measures, especially those of a more technical nature, were either very difficult to computed in the automation system integration, or did not provide sufficient guidance to identify a practice to be used.

Agent-technologies have been used for higher-level decision making in addition to carrying out lower-level automation and control functions in industrial systems. Recent research has identified a number of architectural patterns for the use of agents in industrial automation systems but these practices vary in several ways, including how closely agents are coupled with physical systems and their control functions. Such practices may play a pivotal role in the Cyber-Physical System integration and interaction. Hence, there is a clear need for a common set of criteria for assessing available practices and identifying a bestfit practice for a given industrial use case. Unfortunately, no such common criteria exist currently. This work proposes an assessment criteria approach as well as a methodology to enable the use case based selection of a best practice for integrating agents and industrial systems. The software product quality model proposed by the ISO/IEC 25010 family of standards is used as starting point and is put in the industrial automation context. Subsequently, the proposed methodology is applied, and a survey of experts in the domain is carried out, in order to reveal some insights on the key characteristics of the subject matter.

Cyber-attacks on Industrial Automation and Control Systems (IACS) are rising in numbers and sophistication. Embedded controller devices such as Programmable Logic Controllers (PLCs), which are central to controlling physical processes, must be secured against attacks on confidentiality, integrity and availability. The focus of this paper is to add design-level support for security in IACS applications, especially around inter-PLC communications. We propose an end-to-end solution to develop IACS applications with inherent, and parametric support for security. Built using the IEC 61499 Function Blocks standard, this solution allows us to annotate certain communications as ‘secure’ during design time. When the application is compiled, these annotations are transformed into a security layer that implements encrypted communication between PLCs. In this paper, we implement a part of this security layer focussed on confidentiality, called Confidentiality Layer for Function Blocks (CL4FB), which provides a range of encryption/decryption and secure key exchange functionalities. We study the impact of using CL4FB in IACS applications with real-time constraints. Through a case study focussing on protection functions in smart-grids, we show that varying levels of confidentiality can be achieved while also meeting hard real-time deadlines.

Energy consumption in large scale data centers (LSDCs) doubled from 2000 to 2006 reaching 61 TerraWatt-hour (TWh) per year. Nearly all of the energy consumed by IT equipment dissipates as heat from the servers, creating a real problem of efficiently cooling LSDCs. Reducing the Power Usage Effectiveness (PUE) of a data center by even small fractions significantly reduces greenhouse gas emissions. We propose a novel Pulsed Variable Flow Rate (PVFR) dynamic cooling control strategy for liquid cooled LSDCs, based on the principle of pulsed power delivery instead of continuous power supply. We built a proprietary simulation software called DynaCool using model-driven design to evaluate the effectiveness of PVFR approach. An early evaluation shows that DynaCool achieves a PUE reduction of at least 15.4% over existing static and variable flow rate cooling control strategies. Assuming an adoption rate of 10%, PVFR would yield power savings of 1.88 TWh or greenhouse gas emission savings of 284,000 tons per year.

In the mobile internet era, location-based services have been widely used in mobile applications for delivering novel services to end-users. As battery technologies have long been the bottleneck holding back the development of mobile applications, a large body of research has focused on saving energy. This paper proposes an adaptive middleware architecture for mobile applications using location-based services. We have developed this solution after analyzing popular positioning techniques, such as GPS, Wi-Fi, and Cell Towers. The proposed middleware is adopted to connect the upper layer of location-based applications with the lower hardware layer of the host mobile phone. Adaptive location sensor policies, scenarios adapters and mechanism managers are used to identify the optimal energy-saving positioning methods. To verify the validity of this novel architecture, a preliminary application with GPS and Cell-Tower functionality is implemented and analyzed. The proposed architecture is evaluated both in theory and by experimentation.

The IEC 61499 standard enables the model-based design of complex industrial automation systems, in which a model of the controlled physical processes called a plant, is codeveloped with the controller. However, the existing design flow does not address functional safety issues, which include limiting risk to acceptable levels. Standards like IEC 61508 provide safety guidelines for measuring and managing risk to acceptable ranges using quantitative or probabilistic methods for hardware, and qualitative or systematic analysis techniques for software. Such analyses are inadequate in situations where safety depends on both hardware and software. This paper proposes a unifying model-based approach for the quantitative and qualitative analysis of IEC 61499 designs. The approach combines Markov analysis and model checking to estimate quantified risk and is more expressive than traditional analyses like reliability block diagrams. At design level, unified safety requirements are captured using safety blocks, which is an extension of the IEC 61499 basic blocks. The PRISM model checker is used to analyze the system, based on a sound conversion of IEC 61499 designs into PRISM models. A tool-chain enabling the proposed approach shows encouraging benchmarking results confirming the feasibility of unified analysis.

Cyber-Physical Systems are embedded computers that control complex physical processes and components while cooperating as agents in distributed networks. Due to the scale and complexity of the interactions that occur within cyber-physical systems, requirements traceability strategies that are accurate and easy to manage are hard to implement and maintain. However, the information traces provide is crucial in managing the development and completeness of an application. Existing requirements management systems do not scale well and traceability is difficult in such highly heterogeneous environments. We present TORUS (Traceability Of Requirements Using Splices), a novel traceability framework that operates outside of yet connects to diverse requirements and development environments. Our approach introduces splices, autonomous traceability data structures that persist trace information through the inevitable changes that occur during system design and development. We demonstrate how this framework can be applied to cyber-physical systems that employ the IEC 61499 Function Blocks architecture. Example requirements are expressed as CESAR boilerplates for a workpiece color sorter system. Formal mathematical models of requirements, splices and function blocks are presented to show how trace information can be mined, delivering important project algorithms and metrics to stakeholders. By capturing not only the current state of the system but also historical information, TORUS allows project teams to see a much richer view of their system's artifacts. Preliminary results indicate that the TORUS framework scales well and that the splices generate metrics that will allow us to perform code-level validation and completeness checking in the future.

IEC 61499 enables component-oriented descriptions of complex industrial processes facilitating model-driven engineering. One aspect that is lacking, however, is the ability to directly express Statecharts-like hierarchy and concurrency within basic function blocks (BFBs). Such features can significantly enhance function blocks and help create more succinct and readable specifications. We propose a new syntactic extension to the standard called hierarchical and concurrent execution control chart (HCECC). A major roadblock for any suggested changes to the standard is the need for compliance. Our approach extends the synchronous execution semantics of IEC 61499, where HCECCs are purely syntactic sugar. Using a revised synchronous semantics, our compiler generates standard compliant C code from HCECCs. Benchmarking and usability studies reveal the relative superiority of the proposed approach over existing approaches.

Architectural challenges in migrating plan-driven projects to agile

The numerous intellectual property blocks of a system-on-a-chip must be integrated so that they can meet system-specific requirements. However, such integration is not guaranteed due to mismatches between IP protocols. Protocol conversion algorithms can generate converters that can guarantee correct system behaviour, but the implementation of converters on-chip remains an open question. IPs can be modelled at several layers of the Open Systems Interconnection (OSI) model. Current protocol conversion algorithms either focus on a single layer or worse, blur the boundaries between these layers. We propose a formal framework that allows generating implementable converters for IP protocols modelled at different OSI layers using any existing converter generation algorithm. We apply the framework to an existing conversion algorithm and discuss how it can be as readily used with other algorithms.

Industrial cyber-physical systems require complex distributed software to orchestrate many heterogeneous mechatronic components and control multiple physical processes. Industrial automation software is typically developed in a model-driven fashion where abstractions of physical processes called plant models are co-developed and iteratively refined along with the control code. Testing such multi-dimensional systems is extremely difficult because often models might not be accurate, do not correspond accurately with subsequent refinements, and the software must eventually be tested on the real plant, especially in safety-critical systems like nuclear plants. This paper proposes a framework wherein high-level functional requirements are used to automatically generate test cases for designs at all abstraction levels in the model-driven engineering process. Requirements are initially specified in natural language and then analyzed and specified using a formalized ontology. The requirements ontology is then refined along with controller and plant models during design and development stages such that test cases can be generated automatically at any stage. A representative industrial water process system case study illustrates the strengths of the proposed formalism. The requirements meta-model proposed by the CESAR European project is used for requirements engineering while IEC 61131-3 and model-driven concepts are used in the design and development phases. A tool resulting from the proposed framework called REBATE (Requirements Based Automatic Testing Engine) is used to generate and execute test cases for increasingly concrete controller and plant models.

Requirements engineering is the first stage in the development of any system. For safety-critical industrial systems like smart-grids, we must ensure that requirements are properly elicited, defined, analyzed and managed. This paper adapts the requirements framework developed in the CESAR European project, called the CESAR requirements meta-model, to support all aspects of requirements engineering for safety-critical systems. This enables the formalization of requirements in order to automate and assist in many aspects of later stages in the system development life cycle. Using a smart grid system from the FREEDM project, we illustrate that the adapted requirements engineering framework is comprehensive and rich for large safety-critical systems. We find that the use of the IEC 61499 function block standard provides an appropriate system modelling and implementation framework, which complements the strengths of the proposed requirements engineering framework. Traceability links between requirements and components of an IEC 61499 system model can help with automatic test case generation and formal analysis of requirements.

The IEC 61499 Function Block standard describes an architecture to support the development and reuse of software components for distributed and embedded industrial control and automation systems. Often distributed over heterogeneous execution platforms, IEC 61499 applications are highly re-configurable; users can map individual function blocks to run on any available device. However, the standard does not allow differentiating between the capabilities of different devices in a heterogeneous platform. In this paper, we present a framework that facilitates the utilization of device-specific capabilities during the design of function block applications. Device capabilities are wrapped-up in Basic function blocks linking to low-level device drivers, allowing designers to access device features with ease during the design phase. The framework is completely compatible with the IEC 61499 standard, and remains highly flexible. As a case study, we show how function block applications utilizing low-level capabilities of Raspberry Pi devices can be written and deployed using the Holobloc FBDK development environment. This particular setting of using function blocks to program the Raspberry Pi also results in an ideal, low-cost research and teaching platform for distributed computers.

We present a scalable approach to automatically re-configure evolving IEC 61499 systems for deployment onto an available set of resources. We capture system architecture and high-level configuration requirements formally, and use an efficient SMT-based constraint resolution to generate a valid system configuration. Any changes in the system architecture, configuration requirements, or resources are automatically translated into a minimal set of updated constraints, allowing a faster reconfiguration as compared to a monolithic approach where the whole system is re-configured. We show the feasibility of our approach by studying an airport baggage handling system developed using the IEC 61499 standard.

We face a glut of languages for programming distributed software today. However, only a few languages have proven their potential with wider practical use in different domains of computing. We picked two such languages, meant for different domains, to see if they could cross-pollinate and enrich one another. Specifically, we chose SystemJ, a language to program distributed embedded systems, and IEC61499, the next generation standard for distributed industrial automation control software. Unsurprisingly, we found similar structures and artifacts between the two. We also found significant differences mainly due to differing domain-specific requirements. This comparison leads to observations and guidelines for improving both languages, and we discuss directions towards an “ideal” distributed software programming language.

A system-on-chip (SoC) contains numerous intellectual property blocks, or IPs. Protocol mismatches between IPs may affect the system-level functionality of the SoC. Mismatches are addressed by introducing converters to control inter-IP interactions. Current approaches towards converter generation find limited practical application as they use restrictive models, lack formal rigour, handle a small subset of commonly encountered mismatches, and/or are not scalable. We propose a formal technique for SoC design using incremental converter synthesis . The proposed formulation provides precise models for protocols and requirements, and provides a scalable algorithm that allows adding multiple components and requirements to an SoC incrementally. We prove that the technique is sound and complete. Experimental results obtained using real-life AMBA benchmarks show the scalability and wide range of mismatches handled by our approach.

Safety-critical systems require guarantees on their worst-case execution times. This requires modelling of speculative hardware features such as caches that are tailored to improve the average-case performance, while ignoring the worst case, which complicates the Worst Case Execution Time (WCET) analysis problem. Existing approaches that precisely compute WCET suffer from state-space explosion. In this paper, we present a novel cache analysis technique for direct-mapped instruction caches with the same precision as the most precise techniques, while improving analysis time by up to 240 times. This improvement is achieved by analysing individual control points separately, and carrying out optimisations that are not possible with existing techniques.

Systems-on-chip (SoCs) contain multiple interconnected and interacting components. In this paper, we present a compositional approach for the integration of multiple components with a wide range of protocol mismatches into a single SoC. We show how SoC construction can be done in single-step when all components are integrated at once or it can also be performed incrementally by adding components to an already integrated design. Using a number of AMBA IPs, we show that the proposed framework is able to perform protocol conversion in many cases where existing approaches fail.

Static computation of the worst-case reaction time (WCRT) is required for the real-time execution of synchronous programs. Existing approaches use model checking or integer linear programming. we formulate this as an abstraction-based reachability analysis yielding a lower worst case complexity. Benchmarking shows a significant overall speed-up of 64-times over existing approaches.

The automated design of SoCs from pre-selected IPs that may require different clocks is challenging because of the following issues. Firstly, protocol mismatches between IPs need to be resolved automatically before IPs are integrated. Secondly, the presence of multiple clocks makes the protocol conversion even more difficult. Thirdly, it is desirable that the resulting integration is correct-by-construction, i.e., the resulting SoC satisfies given system-level specifications. All of these issues have been studied extensively, although not in a unifying manner. In this paper we propose a framework based on protocol conversion that addresses all these issues. We have extensively studied many SoC design problems and show that the proposed methodology is capable of handling them better than other known approaches. A significant contribution of the proposed approach is that it nicely generalizes many existing techniques for formal SoC design and integrates them into a single approach.