Artificial Intelligence in Cyber-Physical Security and Intelligent Surveillance: A Comprehensive Survey
Article Sidebar
Main Article Content
Abstract
Modern times require smart solutions for crime detection to enhance public security levels because crime detection remains a fundamental challenge. Traditional surveillance relies on human monitoring but has weaknesses due to time consumption and human error. The automated crime detection system runs on machine learning and deep learning platforms using Convolutional Neural Networks and Recurrent Neural Networks, along with YOLO, Faster R-CNN, and the Azure Face API. The AI models perform two functions: detecting suspicious activities and identifying weapons and faces, and recognising patterns of criminal behaviour. The combination of local servers and cloud platforms powered by AI detects crimes precisely in real time through enhanced monitoring, improves accuracy, and prevents false alarms, while also enabling broader crime-prevention capabilities.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Venkatesh, S., Anand, A., Gokul S., Ramakrishnan, A., & Vineeth Vijayaraghavan. (2020). Real-time Surveillance-Based Crime Detection for Edge Devices. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 801–809. DOI: 10.5220/0008990108010809
Shirsat, S., Naik, A., Tamse, D., Yadav, J., Shetgaonkar, P., & Aswale, S. (2019, March 1). Proposed System for Criminal Detection and Recognition on CCTV Data Using Cloud and Machine Learning. IEEE Xplore. DOI: 10.1109/ViTECoN.2019.8899441
Boukabous, M., & Azizi, M. (2023). Image and video-based crime prediction using object detection and deep learning. Bulletin of Electrical Engineering and Informatics, 12(3), 1630–1638. DOI: 10.11591/eei.v12i3.5157
Choudhry, N., Abawajy, J., Huda, S., & Rao, I. (2023). A Comprehensive Survey of Machine Learning Methods for Surveillance Video Anomaly Detection. IEEE Access, 11, 114680–114713. DOI: 10.1109/ACCESS.2023.3321800
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10271300
Chackravarthy, S., Schmitt, S., & Yang, L. (2018, October 1). Intelligent Crime Anomaly Detection in Smart Cities Using Deep Learning. IEEE Xplore. DOI: 10.1109/CIC.2018.00060
Navalgund, U. V., & K., P. (2018). Crime Intention Detection System Using Deep Learning. 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). DOI:10.1109/iccsdet.2018.8821168
Mandalapu, V., Elluri, L., Vyas, P., & Roy, N. (2023). Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions. IEEE Access, 11(60154),60153–60170. DOI: 10.1109/access.2023.3286344
Oghenevovwero Zion Apene, Nachamada Vachaku Blamah, Imuetinyan, G., & Morufu Olalere. (2024). Development of a Mathematical Model for Crime Detection Based on YOLO Network Architecture. International Journal of Computer Applications,186(20),17–24. DOI: 10.5120/ijca2024923621
Choi, S. (2025). A Smart Crime Reporting Bot Using YOLO-Based Weapon Detection and RNN-Based Text Analysis. IJournals: International Journal of Software & Hardware Research in Engineering, 13(1).
https://ijournals.in/wp-content/uploads/2025/01/3.IJSHRE-131301-Choi.pdf
Janakiramaiah, B., Kalyani, G., & Jayalakshmi, A. (2020). Automatic alert generation in a surveillance system for smart city environment using deep learning algorithm. Evolutionary Intelligence. DOI: 10.1007/s12065-020-00353-4
Mudgal, M., Punj, D., & Pillai, A. (2021). Suspicious Action Detection in Intelligent Surveillance System Using Action Attribute Modelling. Journal of Web Engineering. DOI: 10.13052/jwe1540-9589.2017
Ul Haque, I., Alam Bhuiyan, M., Sayed Al Banna, S. M. A., Haq Bhuiyan, M., Alam, K., & Monir, M. F. (2024). A Smart Surveillance System for Facemask Detection Using Custom CNN—BIDNet. IEEE Access, 12, 122769–122784. DOI: 10.1109/access.2024.3407534
Shah, M., Agre, M., Chawdhary, A., & Deone, J. (2022). Smart Surveillance System. SSRN Electronic Journal.
DOI: 10.2139/ssrn.4108852
Xu, J. (2020). A deep learning approach to building an intelligent video surveillance system. Multimedia Tools and Applications.
DOI: 10.1007/s11042-020-09964-6
Veesam, S. B., & Satish, A. R. (2025). Design of an Integrated Model for Video Summarisation Using Multimodal Fusion and YOLO for Crime Scene Analysis. IEEE Access, 13(25008), 25008–25025. DOI: 10.1109/access.2025.3538282
Kim, S., & Park, K. J. (2021). A survey on machine-learning-based security design for cyber-physical systems. In Applied Sciences (Switzerland) (Vol. 11, Issue 12). MDPI AG. DOI: 10.3390/app11125458
Raza, A., Memon, S., Nizamani, M. A., & Hussain Shah, M. (2022). Machine Learning-Based Security Solutions for Critical Cyber-Physical Systems. 2022 10th International Symposium on Digital Forensics and Security (ISDFS), 1–6. DOI: 10.1109/ISDFS55398.2022.9800811