Explainable AI and machine learning for robust cybersecurity in smart cities

An emerging application of such new technologies is in urban development, with cities increasingly utilizing them to address social, environmental, and urban issues. IoT has paved the way for Smart Cities,  while AI-fueled big data has revolutionized progressive urbanization. However, initiatives to...

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Main Authors: Shruti Gupta, Jyotsna Singh, Rashmi Agrawal, Usha Batra
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-12-01
Series:Cyber Security and Applications
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772918425000219
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author Shruti Gupta
Jyotsna Singh
Rashmi Agrawal
Usha Batra
author_facet Shruti Gupta
Jyotsna Singh
Rashmi Agrawal
Usha Batra
author_sort Shruti Gupta
collection DOAJ
description An emerging application of such new technologies is in urban development, with cities increasingly utilizing them to address social, environmental, and urban issues. IoT has paved the way for Smart Cities,  while AI-fueled big data has revolutionized progressive urbanization. However, initiatives to promote technology must be balanced by principles of sustainability and livability. As deep learning has advanced rapidly, creating increasingly sophisticated technologies has led to highly complex — and often opaque — models that can be difficult to interpret. It becomes increasingly difficult to establish trust and maintain transparency when decision-making systems are based on such opaque and complex structures. This article explores the urban promise of AI and presents a new framework infusion of AI into cityscapes. The new direction is socially oriented through the inclusion of elements such as values, urban metabolism, and governance. A systematic review of machine-learning applications in cybersecurity also discusses the importance of explainability for overcoming the challenges it entails. The importance of assuring the explainability, interpretability, and intelligibility of autonomous systems will also be part of this discussion, especially in the context of developing smart cities using AI-based technologies.
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publisher KeAi Communications Co., Ltd.
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series Cyber Security and Applications
spelling doaj-art-a9faa8312cec47b2a8db1e7a73685cea2025-08-22T04:58:57ZengKeAi Communications Co., Ltd.Cyber Security and Applications2772-91842025-12-01310010410.1016/j.csa.2025.100104Explainable AI and machine learning for robust cybersecurity in smart citiesShruti Gupta0Jyotsna Singh1Rashmi Agrawal2Usha Batra3School of Computer Applications, Manav Rachna International Institute of Research and Studies, Sector 43, Faridabad, Haryana 121004, India; Corresponding author.Department of Computer Science and Engineering, Narsee Monjee Institute of Management Studies, Sarangpur, Chandigarh, IndiaSchool of Computer Applications, Manav Rachna International Institute of Research and Studies, Sector 43, Faridabad, Haryana 121004, IndiaDepartment of Computer Science and Engineering Shri Vishwakarma Skill University, Palwal, Haryana 121102, IndiaAn emerging application of such new technologies is in urban development, with cities increasingly utilizing them to address social, environmental, and urban issues. IoT has paved the way for Smart Cities,  while AI-fueled big data has revolutionized progressive urbanization. However, initiatives to promote technology must be balanced by principles of sustainability and livability. As deep learning has advanced rapidly, creating increasingly sophisticated technologies has led to highly complex — and often opaque — models that can be difficult to interpret. It becomes increasingly difficult to establish trust and maintain transparency when decision-making systems are based on such opaque and complex structures. This article explores the urban promise of AI and presents a new framework infusion of AI into cityscapes. The new direction is socially oriented through the inclusion of elements such as values, urban metabolism, and governance. A systematic review of machine-learning applications in cybersecurity also discusses the importance of explainability for overcoming the challenges it entails. The importance of assuring the explainability, interpretability, and intelligibility of autonomous systems will also be part of this discussion, especially in the context of developing smart cities using AI-based technologies.http://www.sciencedirect.com/science/article/pii/S2772918425000219Smart citiesBig dataInternet of thingsExplainable AIMachine learning
spellingShingle Shruti Gupta
Jyotsna Singh
Rashmi Agrawal
Usha Batra
Explainable AI and machine learning for robust cybersecurity in smart cities
Cyber Security and Applications
Smart cities
Big data
Internet of things
Explainable AI
Machine learning
title Explainable AI and machine learning for robust cybersecurity in smart cities
title_full Explainable AI and machine learning for robust cybersecurity in smart cities
title_fullStr Explainable AI and machine learning for robust cybersecurity in smart cities
title_full_unstemmed Explainable AI and machine learning for robust cybersecurity in smart cities
title_short Explainable AI and machine learning for robust cybersecurity in smart cities
title_sort explainable ai and machine learning for robust cybersecurity in smart cities
topic Smart cities
Big data
Internet of things
Explainable AI
Machine learning
url http://www.sciencedirect.com/science/article/pii/S2772918425000219
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