Pub Date : 2024-01-01DOI: 10.1109/mce.2022.3164529
C. Dehury, S. Srirama, Praveen Kumar Donta, S. Dustdar
The devices at the edge of a network are not only responsible for sensing the surrounding environment but are also made intelligent enough to learn and react to the environment. Clustered Edge Intelligence (CEI) emphasizes intelligence-centric clustering instead of device-centric clustering. It allows the devices to share their knowledge and events with other devices and the remote fog or cloud servers. However, recent advancements facilitate the traceability of the events’ history by analyzing edge devices’ event logs, which are compute intensive and easy to alter. This article focuses on a blockchain-based solution for CEI that makes the edge devices’ events history immutable and easily traceable. This article further explains how the edge devices’ activities and the environmental data can be secured from the source device to the cloud servers. Such a secured CEI mechanism can be applied in establishing a transparent and efficient smart city, supply chain, logistics, and transportation systems.
{"title":"Securing clustered edge intelligence with blockchain","authors":"C. Dehury, S. Srirama, Praveen Kumar Donta, S. Dustdar","doi":"10.1109/mce.2022.3164529","DOIUrl":"https://doi.org/10.1109/mce.2022.3164529","url":null,"abstract":"The devices at the edge of a network are not only responsible for sensing the surrounding environment but are also made intelligent enough to learn and react to the environment. Clustered Edge Intelligence (CEI) emphasizes intelligence-centric clustering instead of device-centric clustering. It allows the devices to share their knowledge and events with other devices and the remote fog or cloud servers. However, recent advancements facilitate the traceability of the events’ history by analyzing edge devices’ event logs, which are compute intensive and easy to alter. This article focuses on a blockchain-based solution for CEI that makes the edge devices’ events history immutable and easily traceable. This article further explains how the edge devices’ activities and the environmental data can be secured from the source device to the cloud servers. Such a secured CEI mechanism can be applied in establishing a transparent and efficient smart city, supply chain, logistics, and transportation systems.","PeriodicalId":54330,"journal":{"name":"IEEE Consumer Electronics Magazine","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62339720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/mce.2023.3256640
A. Nanda, S. W. Shah, J. Jeong, R. Doss, Jeb Webb
Identity proofing is often a prerequisite for accessing important services (e.g., opening a bank account). The current pandemic has highlighted the need for remote identity proofing (RIDP) that can enable applicants to prove their identity from anywhere, without the need for a special facility. However, the requirements set out by the National Institute of Standards and Technology for the highest level of assurance in RIDP systems currently rule out fully automated and remote solutions, as they are not yet foolproof. This article aims to propose a way forward for pervasive RIDP solutions and highlights the requirements for accomplishing the highest level of assurance in verifying identity. We pinpoint relevant issues and threats along with the current state-of-the-art countermeasures and discuss what else needs to be done to enable ubiquitous remote identity-proofing systems.
{"title":"Towards Higher Levels of Assurance in Remote Identity Proofing","authors":"A. Nanda, S. W. Shah, J. Jeong, R. Doss, Jeb Webb","doi":"10.1109/mce.2023.3256640","DOIUrl":"https://doi.org/10.1109/mce.2023.3256640","url":null,"abstract":"Identity proofing is often a prerequisite for accessing important services (e.g., opening a bank account). The current pandemic has highlighted the need for remote identity proofing (RIDP) that can enable applicants to prove their identity from anywhere, without the need for a special facility. However, the requirements set out by the National Institute of Standards and Technology for the highest level of assurance in RIDP systems currently rule out fully automated and remote solutions, as they are not yet foolproof. This article aims to propose a way forward for pervasive RIDP solutions and highlights the requirements for accomplishing the highest level of assurance in verifying identity. We pinpoint relevant issues and threats along with the current state-of-the-art countermeasures and discuss what else needs to be done to enable ubiquitous remote identity-proofing systems.","PeriodicalId":54330,"journal":{"name":"IEEE Consumer Electronics Magazine","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62340129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/mce.2023.3256641
Utsab Khakurel, D. Rawat
Sensor-powered devices offer safe global connections, cloud scalability and flexibility, and new business value driven by data. The constraints that have historically obstructed major innovations in technology can be addressed by advancements in artificial intelligence (AI) and machine learning, cloud, quantum computing, and the ubiquitous availability of data. Edge artificial intelligence refers to the deployment of AI applications on the edge device near the data source rather than in a cloud computing environment. Although edge data have been utilized to make inferences in real time through predictive models, real-time machine learning has not yet been fully adopted. Real-time machine learning utilizes real-time data to learn on the go, which helps in faster and more accurate real-time predictions and eliminates the need to store data eradicating privacy issues. In this article, we present the practical prospect of developing a physical threat detection system using real-time edge data from security cameras/sensors to improve the accuracy, efficiency, reliability, security, and privacy of the real-time inference model.
{"title":"Real-Time Physical Threat Detection on Edge Data Using Online Learning","authors":"Utsab Khakurel, D. Rawat","doi":"10.1109/mce.2023.3256641","DOIUrl":"https://doi.org/10.1109/mce.2023.3256641","url":null,"abstract":"Sensor-powered devices offer safe global connections, cloud scalability and flexibility, and new business value driven by data. The constraints that have historically obstructed major innovations in technology can be addressed by advancements in artificial intelligence (AI) and machine learning, cloud, quantum computing, and the ubiquitous availability of data. Edge artificial intelligence refers to the deployment of AI applications on the edge device near the data source rather than in a cloud computing environment. Although edge data have been utilized to make inferences in real time through predictive models, real-time machine learning has not yet been fully adopted. Real-time machine learning utilizes real-time data to learn on the go, which helps in faster and more accurate real-time predictions and eliminates the need to store data eradicating privacy issues. In this article, we present the practical prospect of developing a physical threat detection system using real-time edge data from security cameras/sensors to improve the accuracy, efficiency, reliability, security, and privacy of the real-time inference model.","PeriodicalId":54330,"journal":{"name":"IEEE Consumer Electronics Magazine","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62340138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/MCE.2022.3141068
A. Hazra, A. Alkhayyat, Mainak Adhikari
The blockchain is one of the most promising and artistic cybersecurity solutions. It has been practiced in a variety of reinforcements, including healthcare, transportation, and Internet of Things (IoT) applications. However, blockchain has a colossal scalability challenge, limiting its ability to control services with high transaction volumes. Edge computing, on the other hand, was designed to allow cloud services and resources to be deployed at the network's edge, although it now faces issues in terms of decentralized security and management. The unification of edge computing and blockchain within one solution jar provides a vast scale of storage systems, database servers, and authenticity computation toward the end in a safe fashion. This article provides an overview of the secure IoT framework, paradigms, enablers, and security problems of combining blockchain and intelligent edge computing. Finally, broader viewpoints for future research directions are investigated.
{"title":"Blockchain for Cybersecurity in Edge Networks","authors":"A. Hazra, A. Alkhayyat, Mainak Adhikari","doi":"10.1109/MCE.2022.3141068","DOIUrl":"https://doi.org/10.1109/MCE.2022.3141068","url":null,"abstract":"The blockchain is one of the most promising and artistic cybersecurity solutions. It has been practiced in a variety of reinforcements, including healthcare, transportation, and Internet of Things (IoT) applications. However, blockchain has a colossal scalability challenge, limiting its ability to control services with high transaction volumes. Edge computing, on the other hand, was designed to allow cloud services and resources to be deployed at the network's edge, although it now faces issues in terms of decentralized security and management. The unification of edge computing and blockchain within one solution jar provides a vast scale of storage systems, database servers, and authenticity computation toward the end in a safe fashion. This article provides an overview of the secure IoT framework, paradigms, enablers, and security problems of combining blockchain and intelligent edge computing. Finally, broader viewpoints for future research directions are investigated.","PeriodicalId":54330,"journal":{"name":"IEEE Consumer Electronics Magazine","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139126343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/mce.2022.3168997
Sujeet S. Jagtap, Shankar Sriram V. S., K. Kotecha, S. V.
Demanding scientific evolution and undisrupted resource requirement of consumers signified the amalgamation of mechanical production, mass production, and digitalized production for the fourth industrial revolution, “Industry 4.0.” Critical infrastructures that operate and govern industrial sectors and public utilities, such as water desalination plants, smart grids, and gas pipelines, incorporated this cognitive-mechatronic augmentation for the seamless integration of software, control components, and production employees to increase the productivity scale. Although connectivity, automation, and optimization made industrial sectors realize the full potential of smart manufacturing, the inclusion of supervisory control and data acquisition systems into cyberspace expanded the attack vectors that made industrial control systems the prime target for cyber-attackers. Conventional security solutions, such as firewalls, traditional intrusion-detection systems, and antivirus, have been proposed and developed by the research community acted as a proficient line of cyber-defense. However, protecting critical infrastructures from heterogeneous cyber-attacks for resilient operability still pose a significant research challenge. In addition, although machine learning and deep-learning-based intrusion-detection models have been proposed and optimized in the literature, operational viability still poses a significant setback for real-time intrusion detection on industrial control systems. By considering the limitations identified in the literature, a stacked deep-learning model is proposed and validated over laboratory-scale industrial datasets. Furthermore, this article provides an overview of cyber-physical systems, conventional security solutions, and their challenges in identifying unseen exploits. As a concluding remark, JARA: a hybrid opensource deployment-ready intelligent intrusion-detection system, has been presented that feasibly detects the HnS IIoT malware when deployed on a Linux virtual machine.
{"title":"Securing Industrial Control Systems from Cyber-Attacks: A Stacked Neural-Network based Approach","authors":"Sujeet S. Jagtap, Shankar Sriram V. S., K. Kotecha, S. V.","doi":"10.1109/mce.2022.3168997","DOIUrl":"https://doi.org/10.1109/mce.2022.3168997","url":null,"abstract":"Demanding scientific evolution and undisrupted resource requirement of consumers signified the amalgamation of mechanical production, mass production, and digitalized production for the fourth industrial revolution, “Industry 4.0.” Critical infrastructures that operate and govern industrial sectors and public utilities, such as water desalination plants, smart grids, and gas pipelines, incorporated this cognitive-mechatronic augmentation for the seamless integration of software, control components, and production employees to increase the productivity scale. Although connectivity, automation, and optimization made industrial sectors realize the full potential of smart manufacturing, the inclusion of supervisory control and data acquisition systems into cyberspace expanded the attack vectors that made industrial control systems the prime target for cyber-attackers. Conventional security solutions, such as firewalls, traditional intrusion-detection systems, and antivirus, have been proposed and developed by the research community acted as a proficient line of cyber-defense. However, protecting critical infrastructures from heterogeneous cyber-attacks for resilient operability still pose a significant research challenge. In addition, although machine learning and deep-learning-based intrusion-detection models have been proposed and optimized in the literature, operational viability still poses a significant setback for real-time intrusion detection on industrial control systems. By considering the limitations identified in the literature, a stacked deep-learning model is proposed and validated over laboratory-scale industrial datasets. Furthermore, this article provides an overview of cyber-physical systems, conventional security solutions, and their challenges in identifying unseen exploits. As a concluding remark, JARA: a hybrid opensource deployment-ready intelligent intrusion-detection system, has been presented that feasibly detects the HnS IIoT malware when deployed on a Linux virtual machine.","PeriodicalId":54330,"journal":{"name":"IEEE Consumer Electronics Magazine","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62339828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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