A. S. Mohammed, P. Reinecke, P. Burnap, O. Rana, Eirini Anthi
There has been significant interest within the offshore oil and gas industry to utilise Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS). There has also been a corresponding increase in cyberattacks targeted at oil and gas companies. Offshore oil production requires remote access to and control of large and complex hardware resources. This is achieved by integrating ICPS, Supervisory, Control and Data Acquisition (SCADA) systems, and IIoT technologies. A successful cyberattack against an oil and gas (O&G) offshore asset could have a major impact on the environment, marine ecosystem and safety of personnel. Any disruption to the world’s supply of O&G can also have an effect on oil prices and the global economy. We describe the cyberattack surface within the oil and gas industry, discussing emerging trends in the offshore sub-sector and provide a historical perspective of known cyberattacks. We also present a case study of a subsea control system architecture typically used in offshore O&G operations and highlight potential vulnerabilities affecting the components of the system. This study is the first to provide a detailed analysis of attack vectors in a subsea control system. The analysis provided can be used to understand key vulnerabilities in such systems and may be used to implement efficient mitigation methods.
{"title":"Cybersecurity Challenges in the Offshore Oil and Gas Industry: An Industrial Cyber-Physical Systems (ICPS) Perspective","authors":"A. S. Mohammed, P. Reinecke, P. Burnap, O. Rana, Eirini Anthi","doi":"10.1145/3548691","DOIUrl":"https://doi.org/10.1145/3548691","url":null,"abstract":"There has been significant interest within the offshore oil and gas industry to utilise Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS). There has also been a corresponding increase in cyberattacks targeted at oil and gas companies. Offshore oil production requires remote access to and control of large and complex hardware resources. This is achieved by integrating ICPS, Supervisory, Control and Data Acquisition (SCADA) systems, and IIoT technologies. A successful cyberattack against an oil and gas (O&G) offshore asset could have a major impact on the environment, marine ecosystem and safety of personnel. Any disruption to the world’s supply of O&G can also have an effect on oil prices and the global economy. We describe the cyberattack surface within the oil and gas industry, discussing emerging trends in the offshore sub-sector and provide a historical perspective of known cyberattacks. We also present a case study of a subsea control system architecture typically used in offshore O&G operations and highlight potential vulnerabilities affecting the components of the system. This study is the first to provide a detailed analysis of attack vectors in a subsea control system. The analysis provided can be used to understand key vulnerabilities in such systems and may be used to implement efficient mitigation methods.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116953256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaodong Yang, O. Beg, M. Kenigsberg, Taylor T. Johnson
Automata-based modeling of hybrid and cyber-physical systems (CPS) is an important formal abstraction amenable to algorithmic analysis of its dynamic behaviors, such as in verification, fault identification, and anomaly detection. However, for realistic systems, especially industrial ones, identifying hybrid automata is challenging, due in part to inferring hybrid interactions, which involves inference of both continuous behaviors, such as through classical system identification, as well as discrete behaviors, such as through automata (e.g., L*) learning. In this paper, we propose and evaluate a framework for inferring and validating models of deterministic hybrid systems with linear ordinary differential equations (ODEs) from input/output execution traces. The framework contains algorithms for the approximation of continuous dynamics in discrete modes, estimation of transition conditions, and the inference of automata mode merging. The algorithms are capable of clustering trace segments and estimating their dynamic parameters, and meanwhile, deriving guard conditions that are represented by multiple linear inequalities. Finally, the inferred model is automatically converted to the format of the original system for the validation. We demonstrate the utility of this framework by evaluating its performance in several case studies as implemented through a publicly available prototype software framework called HAutLearn and compare it with a membership-based algorithm.
{"title":"A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces","authors":"Xiaodong Yang, O. Beg, M. Kenigsberg, Taylor T. Johnson","doi":"10.1145/3470455","DOIUrl":"https://doi.org/10.1145/3470455","url":null,"abstract":"Automata-based modeling of hybrid and cyber-physical systems (CPS) is an important formal abstraction amenable to algorithmic analysis of its dynamic behaviors, such as in verification, fault identification, and anomaly detection. However, for realistic systems, especially industrial ones, identifying hybrid automata is challenging, due in part to inferring hybrid interactions, which involves inference of both continuous behaviors, such as through classical system identification, as well as discrete behaviors, such as through automata (e.g., L*) learning. In this paper, we propose and evaluate a framework for inferring and validating models of deterministic hybrid systems with linear ordinary differential equations (ODEs) from input/output execution traces. The framework contains algorithms for the approximation of continuous dynamics in discrete modes, estimation of transition conditions, and the inference of automata mode merging. The algorithms are capable of clustering trace segments and estimating their dynamic parameters, and meanwhile, deriving guard conditions that are represented by multiple linear inequalities. Finally, the inferred model is automatically converted to the format of the original system for the validation. We demonstrate the utility of this framework by evaluating its performance in several case studies as implemented through a publicly available prototype software framework called HAutLearn and compare it with a membership-based algorithm.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126381256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Brik, M. Messaadia, M. Sahnoun, B. Bettayeb, M. Benatia
Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things and Cyber-Physical-Production-Systems, to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, and so on. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g., a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems. In this article, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) that are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level that provides local data processing support with low latency. Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real time.
{"title":"Fog-supported Low-latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach","authors":"B. Brik, M. Messaadia, M. Sahnoun, B. Bettayeb, M. Benatia","doi":"10.1145/3477272","DOIUrl":"https://doi.org/10.1145/3477272","url":null,"abstract":"Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things and Cyber-Physical-Production-Systems, to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, and so on. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g., a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems. In this article, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) that are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level that provides local data processing support with low latency. Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real time.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126843883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The proliferation of cyber-physical systems (CPSs) and the advancement of the Internet of Things (IoT) technologies have led to explosive digitization of the industrial sector. It offers promising perspectives for high reliability, availability, maintainability, and safety production process, but also makes the systems more complex and challenging for health assessment. To deal with these challenges, one needs to develop a robust approach to monitor and assess the system’s health state. In this article, a practical and effective hybrid deep learning multi-task framework integrating the advantages of convolutional neural network (CNN) and long short-term memory (LSTM) neural network to reflect the relatedness of remaining useful life prediction with health status detection process for complex multi-object systems in CPS environment is developed. The CNN is used as a feature extractor to compress condition monitoring data and directly extract significant spatiotemporal features from raw multi-sensory input data. The LSTM is used to capture long-term temporary dependency features. The advantages of the proposed hybrid deep learning framework have been verified on the popular NASA’s C-MAPSS dataset. The experimental study compares this approach to the existing methods using the same dataset. The results suggest that the proposed hybrid CNN-LSTM model is superior to existing methods, including traditional machine learning and deep learning-based methods. The proposed framework can provide strong support for the health management and maintenance strategy development of complex multi-object systems.
{"title":"A Hybrid Deep Learning Framework for Intelligent Predictive Maintenance of Cyber-physical Systems","authors":"M. Shcherbakov, C. Sai","doi":"10.1145/3486252","DOIUrl":"https://doi.org/10.1145/3486252","url":null,"abstract":"The proliferation of cyber-physical systems (CPSs) and the advancement of the Internet of Things (IoT) technologies have led to explosive digitization of the industrial sector. It offers promising perspectives for high reliability, availability, maintainability, and safety production process, but also makes the systems more complex and challenging for health assessment. To deal with these challenges, one needs to develop a robust approach to monitor and assess the system’s health state. In this article, a practical and effective hybrid deep learning multi-task framework integrating the advantages of convolutional neural network (CNN) and long short-term memory (LSTM) neural network to reflect the relatedness of remaining useful life prediction with health status detection process for complex multi-object systems in CPS environment is developed. The CNN is used as a feature extractor to compress condition monitoring data and directly extract significant spatiotemporal features from raw multi-sensory input data. The LSTM is used to capture long-term temporary dependency features. The advantages of the proposed hybrid deep learning framework have been verified on the popular NASA’s C-MAPSS dataset. The experimental study compares this approach to the existing methods using the same dataset. The results suggest that the proposed hybrid CNN-LSTM model is superior to existing methods, including traditional machine learning and deep learning-based methods. The proposed framework can provide strong support for the health management and maintenance strategy development of complex multi-object systems.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114839371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jackson Codispoti, A. R. Khamesi, Nelson Penn, S. Silvestri, Eura Shin
With the acceleration of Information and Communication Technologies and the Internet-of-Things paradigm, smart residential environments, also known as smart homes, are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition. Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert, always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this article, we develop a SAL algorithm, called K-Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness, representativeness, and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.
{"title":"Learning from Non-experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes","authors":"Jackson Codispoti, A. R. Khamesi, Nelson Penn, S. Silvestri, Eura Shin","doi":"10.1145/3491241","DOIUrl":"https://doi.org/10.1145/3491241","url":null,"abstract":"With the acceleration of Information and Communication Technologies and the Internet-of-Things paradigm, smart residential environments, also known as smart homes, are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition. Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert, always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this article, we develop a SAL algorithm, called K-Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness, representativeness, and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128570583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Khodabandehloo, A. Alimohammadi, Daniele Riboni
Thanks to the seamless integration of sensing, networking, and artificial intelligence, cyber-physical systems promise to improve healthcare by increasing efficiency and reducing costs. Specifically, cyber-physical systems are being increasingly applied in smart-homes to support independent and healthy aging. Due to the growing prevalence of noncommunicable diseases in the senior population, a key application in this domain is the detection of cognitive issues based on sensor data. In this article, we propose a novel cyber-physical system for cognitive assessment in smart-homes. Cognitive evaluation relies on clinical indicators characterizing symptoms of dementia based on the individual’s movement patterns. However, recognizing these patterns in smart-homes is challenging, because movement is constrained by the home layout and obstacles. Since different abnormal patterns are characterized by undulatory-like trajectories, we conjecture that frequency-based locomotion features may more effectively capture these patterns with respect to traditional features in the spatio-temporal domain. Based on this intuition, we introduce novel feature extraction techniques and adopt state-of-the-art machine learning algorithms for short- and long-term cognitive evaluation. Our system includes a user-friendly interface that enables clinicians to inspect the data and predictions. Extensive experiments carried out with a real-world dataset acquired from both cognitively healthy seniors and people with dementia show the superiority of our frequency-based features. Moreover, further experiments with an ensemble method show that prediction accuracy can be enhanced by combining features in the frequency and time domains.
{"title":"FreeSia: A Cyber-physical System for Cognitive Assessment through Frequency-domain Indoor Locomotion Analysis","authors":"E. Khodabandehloo, A. Alimohammadi, Daniele Riboni","doi":"10.1145/3470454","DOIUrl":"https://doi.org/10.1145/3470454","url":null,"abstract":"Thanks to the seamless integration of sensing, networking, and artificial intelligence, cyber-physical systems promise to improve healthcare by increasing efficiency and reducing costs. Specifically, cyber-physical systems are being increasingly applied in smart-homes to support independent and healthy aging. Due to the growing prevalence of noncommunicable diseases in the senior population, a key application in this domain is the detection of cognitive issues based on sensor data. In this article, we propose a novel cyber-physical system for cognitive assessment in smart-homes. Cognitive evaluation relies on clinical indicators characterizing symptoms of dementia based on the individual’s movement patterns. However, recognizing these patterns in smart-homes is challenging, because movement is constrained by the home layout and obstacles. Since different abnormal patterns are characterized by undulatory-like trajectories, we conjecture that frequency-based locomotion features may more effectively capture these patterns with respect to traditional features in the spatio-temporal domain. Based on this intuition, we introduce novel feature extraction techniques and adopt state-of-the-art machine learning algorithms for short- and long-term cognitive evaluation. Our system includes a user-friendly interface that enables clinicians to inspect the data and predictions. Extensive experiments carried out with a real-world dataset acquired from both cognitively healthy seniors and people with dementia show the superiority of our frequency-based features. Moreover, further experiments with an ensemble method show that prediction accuracy can be enhanced by combining features in the frequency and time domains.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131618392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samar F. Abbas, Ahmed Ehsan, Saad Ahmed, Sheraz Ali Khan, Tariq M. Jadoon, Muhammad Hamad Alizai
In developing countries, majority of the households use overhead water tanks to have running water. These water tanks are exposed to the elements, which usually render the tap water uncomfortable to use, given the extreme subtropical weather conditions. Externally weatherproofing these tanks to maintain the groundwater temperature is short-lived, and only results in a marginal (0.5°C–1°C) improvement in tap water temperature. We propose Ashray, an IoT-inspired, intelligent system to minimize the exposure of water to the elements thereby maintaining its temperature close to that of the groundwater. Ashray learns the water demand patterns of a household and pumps water into the overhead tank only when necessary. The predictive, machine learning based, approach of Ashray improves water comfort by up to 8°C in summers and 3°C in winters, on average. Ashray is retrofitted into existing infrastructure with a hardware prototyping cost of $27, whereas it can save up to 16% on water heating costs, through reduction in natural gas consumption, by leveraging groundwater temperature. Moreover, we also consider a transiently-powered Ashray, which uses the energy harvested from the ambient environment, and propose an intermittent data pipeline to improve its prediction accuracy. The transiently-powered Ashray is suitable for long-term deployment, requires minimal maintenance and delivers approximately the same performance. Ashray has the potential to improve the thermal comfort and reduce energy costs for millions of households in developing countries.
{"title":"ASHRAY: Enhancing Water-usage Comfort in Developing Regions using Data-driven IoT Retrofits","authors":"Samar F. Abbas, Ahmed Ehsan, Saad Ahmed, Sheraz Ali Khan, Tariq M. Jadoon, Muhammad Hamad Alizai","doi":"10.1145/3491242","DOIUrl":"https://doi.org/10.1145/3491242","url":null,"abstract":"In developing countries, majority of the households use overhead water tanks to have running water. These water tanks are exposed to the elements, which usually render the tap water uncomfortable to use, given the extreme subtropical weather conditions. Externally weatherproofing these tanks to maintain the groundwater temperature is short-lived, and only results in a marginal (0.5°C–1°C) improvement in tap water temperature. We propose Ashray, an IoT-inspired, intelligent system to minimize the exposure of water to the elements thereby maintaining its temperature close to that of the groundwater. Ashray learns the water demand patterns of a household and pumps water into the overhead tank only when necessary. The predictive, machine learning based, approach of Ashray improves water comfort by up to 8°C in summers and 3°C in winters, on average. Ashray is retrofitted into existing infrastructure with a hardware prototyping cost of $27, whereas it can save up to 16% on water heating costs, through reduction in natural gas consumption, by leveraging groundwater temperature. Moreover, we also consider a transiently-powered Ashray, which uses the energy harvested from the ambient environment, and propose an intermittent data pipeline to improve its prediction accuracy. The transiently-powered Ashray is suitable for long-term deployment, requires minimal maintenance and delivers approximately the same performance. Ashray has the potential to improve the thermal comfort and reduce energy costs for millions of households in developing countries.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115442713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Sánchez, Nils Jörgensen, Martin Törngren, R. Inam, Andrii Berezovskyi, Lei Feng, E. Fersman, Muhammad Rusyadi Ramli, Kaige Tan
Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking—with corresponding multiple interpretations of “edge computing” (e.g., device edge, network edge, distributed cloud). Considering the strong drivers for edge computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g., avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability, are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.
{"title":"Edge Computing for Cyber-physical Systems: A Systematic Mapping Study Emphasizing Trustworthiness","authors":"J. Sánchez, Nils Jörgensen, Martin Törngren, R. Inam, Andrii Berezovskyi, Lei Feng, E. Fersman, Muhammad Rusyadi Ramli, Kaige Tan","doi":"10.1145/3539662","DOIUrl":"https://doi.org/10.1145/3539662","url":null,"abstract":"Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking—with corresponding multiple interpretations of “edge computing” (e.g., device edge, network edge, distributed cloud). Considering the strong drivers for edge computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g., avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability, are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130364242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Great progress has been made in deep learning over the past few years, which drives the deployment of deep learning–based applications into cyber-physical systems. But the lack of interpretability for deep learning models has led to potential security holes. Recent research has found that deep neural networks are vulnerable to well-designed input examples, called adversarial examples. Such examples are often too small to detect, but they completely fool deep learning models. In practice, adversarial attacks pose a serious threat to the success of deep learning. With the continuous development of deep learning applications, adversarial examples for different fields have also received attention. In this article, we summarize the methods of generating adversarial examples in computer vision, speech recognition, and natural language processing and study the applications of adversarial examples. We also explore emerging research and open problems.
{"title":"Research Progress and Challenges on Application-Driven Adversarial Examples: A Survey","authors":"Wei Jiang, Zhiyuan He, Jinyu Zhan, Weijia Pan, Deepak Adhikari","doi":"10.1145/3470493","DOIUrl":"https://doi.org/10.1145/3470493","url":null,"abstract":"Great progress has been made in deep learning over the past few years, which drives the deployment of deep learning–based applications into cyber-physical systems. But the lack of interpretability for deep learning models has led to potential security holes. Recent research has found that deep neural networks are vulnerable to well-designed input examples, called adversarial examples. Such examples are often too small to detect, but they completely fool deep learning models. In practice, adversarial attacks pose a serious threat to the success of deep learning. With the continuous development of deep learning applications, adversarial examples for different fields have also received attention. In this article, we summarize the methods of generating adversarial examples in computer vision, speech recognition, and natural language processing and study the applications of adversarial examples. We also explore emerging research and open problems.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126257878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial Intelligence (AI)-based classifiers rely on Machine Learning (ML) algorithms to provide functionalities that system architects are often willing to integrate into critical Cyber-Physical Systems (CPSs). However, such algorithms may misclassify observations, with potential detrimental effects on the system itself or on the health of people and of the environment. In addition, CPSs may be subject to threats that were not previously known, motivating the need for building Intrusion Detectors (IDs) that can effectively deal with zero-day attacks. Different studies were directed to compare misclassifications of various algorithms to identify the most suitable one for a given system. Unfortunately, even the most suitable algorithm may still show an unsatisfactory number of misclassifications when system requirements are strict. A possible solution may rely on the adoption of meta-learners, which build ensembles of base-learners to reduce misclassifications and that are widely used for supervised learning. Meta-learners have the potential to reduce misclassifications with respect to non-meta learners: however, misleading base-learners may let the meta-learner leaning towards misclassifications and therefore their behavior needs to be carefully assessed through empirical evaluation. To such extent, in this paper we investigate, expand, empirically evaluate, and discuss meta-learning approaches that rely on ensembles of unsupervised algorithms to detect (zero-day) intrusions in CPSs. Our experimental comparison is conducted by means of public datasets belonging to network intrusion detection and biometric authentication systems, which are common IDSs for CPSs. Overall, we selected 21 datasets, 15 unsupervised algorithms and 9 different meta-learning approaches. Results allow discussing the applicability and suitability of meta-learning for unsupervised anomaly detection, comparing metric scores achieved by base algorithms and meta-learners. Analyses and discussion end up showing how the adoption of meta-learners significantly reduces misclassifications when detecting (zero-day) intrusions in CPSs.
{"title":"Meta-Learning to Improve Unsupervised Intrusion Detection in Cyber-Physical Systems","authors":"T. Zoppi, M. Gharib, M. Atif, A. Bondavalli","doi":"10.1145/3467470","DOIUrl":"https://doi.org/10.1145/3467470","url":null,"abstract":"Artificial Intelligence (AI)-based classifiers rely on Machine Learning (ML) algorithms to provide functionalities that system architects are often willing to integrate into critical Cyber-Physical Systems (CPSs). However, such algorithms may misclassify observations, with potential detrimental effects on the system itself or on the health of people and of the environment. In addition, CPSs may be subject to threats that were not previously known, motivating the need for building Intrusion Detectors (IDs) that can effectively deal with zero-day attacks. Different studies were directed to compare misclassifications of various algorithms to identify the most suitable one for a given system. Unfortunately, even the most suitable algorithm may still show an unsatisfactory number of misclassifications when system requirements are strict. A possible solution may rely on the adoption of meta-learners, which build ensembles of base-learners to reduce misclassifications and that are widely used for supervised learning. Meta-learners have the potential to reduce misclassifications with respect to non-meta learners: however, misleading base-learners may let the meta-learner leaning towards misclassifications and therefore their behavior needs to be carefully assessed through empirical evaluation. To such extent, in this paper we investigate, expand, empirically evaluate, and discuss meta-learning approaches that rely on ensembles of unsupervised algorithms to detect (zero-day) intrusions in CPSs. Our experimental comparison is conducted by means of public datasets belonging to network intrusion detection and biometric authentication systems, which are common IDSs for CPSs. Overall, we selected 21 datasets, 15 unsupervised algorithms and 9 different meta-learning approaches. Results allow discussing the applicability and suitability of meta-learning for unsupervised anomaly detection, comparing metric scores achieved by base algorithms and meta-learners. Analyses and discussion end up showing how the adoption of meta-learners significantly reduces misclassifications when detecting (zero-day) intrusions in CPSs.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126984360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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