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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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| 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. |
| format | Article |
| id | doaj-art-a9faa8312cec47b2a8db1e7a73685cea |
| institution | Kabale University |
| issn | 2772-9184 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| 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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