Review of Cloud-Based Public Security Video Investigation Systems: Architecture, Challenges, and Future Directions
DOI:
https://doi.org/10.70454/JRIST.020203Keywords:
Cloud computing, Video surveillance, AI-driven analytics, Cybersecurity Threats , Real-time ProcessingAbstract
Cloud computing has significantly transformed the way people investigate security video images by enhancing the analytical ability of surveillance technology, scaling and efficiency. Ordinary video surveillance systems have limitations in storage, real-time processing, and analysis of data, cloud computing addresses these issues quite satisfactorily. This paper explores different types of cloud computing models, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and its applications in public security video studies. The concept of cloud-based video surveillance systems is mentioned along with the methods of data collection, cloud storage solutions, real-time processing, and analytics powered by artificial intelligence. The paper highlights the key benefits of cloud-based security systems which include improved scalability, cost efficiency, real-time monitoring, and AI-based threat detection. To ensure optimal performance, however, issues such as data privacy issues, cyber attacks, interoperability issues and dependency on constant internet connectivity need to be addressed. The paper also examines Indian case studies that have conducted cloud-based video analysis in the law enforcement system and smart city monitoring programs. By implementing the recently emerging technologies such as artificial intelligence, 5G, and edge computing, cloud-based public security systems can enhance the capability to prevent and respond to crimes. The essay discusses the future that should occur and highlights that regulatory frameworks, moral implementation of AI, and superior cloud protection measures are required.
References
[1] A. A. Abba Ari et al., “Enabling privacy and security in Cloud of Things: Architecture, applications, security & privacy challenges,” Appl. Comput. Informatics, vol. 20, no. 1–2, pp. 119–141, 2024, doi: 10.1016/j.aci.2019.11.005.
[2] J. N. A. M. -, S. P. -, S. V. B. -, and M. D. -, “Enhancing Cloud Compliance: A Machine Learning Approach,” Adv. Int. J. Multidiscip. Res., vol. 2, no. 2, pp. 1–16, 2024, doi: 10.62127/aijmr.2024.v02i02.1036.
[3] Y. Wang, M. Zhu, J. Yuan, G. Wang, and H. Zhou, “The intelligent prediction and assessment of financial information risk in the cloud computing model,” Appl. Comput. Eng., vol. 64, no. 1, pp. 127–133, 2024, doi: 10.54254/2755-2721/64/20241372.
[4] M. Kaleem, M. A. Mushtaq, and S. K. Hussain, “New Efficient Cryptographic Techniques For Cloud Computing Security,” no. June, 2024.
[5] A. Wali and F. Alshehry, “A Survey of Security Challenges in Cloud-Based SCADA Systems,” Computers, vol. 13, no. 4, 2024, doi: 10.3390/computers13040097.
[6] N. Mohammad, “Multi-Cloud Environments : a Comprehensive Study on Encryption Techniques and Access Control,” no. April, 2024.
[7] M. Akbar, I. Ahmad, M. Mirza, M. Ali, and P. Barmavatu, “Enhanced authentication for de-duplication of big data on cloud storage system using machine learning approach,” Cluster Comput., vol. 27, no. 3, pp. 3683–3702, 2024, doi: 10.1007/s10586-023-04171-y.
[8] A. Baur, “European Dreams of the Cloud: Imagining Innovation and Political Control,” Geopolitics, vol. 29, no. 3, pp. 796–820, 2024, doi: 10.1080/14650045.2022.2151902.
[9] Akoh Atadoga, Uchenna Joseph Umoga, Oluwaseun Augustine Lottu, and Enoch Oluwademilade Sodiya, “Evaluating the impact of cloud computing on accounting firms: A review of efficiency, scalability, and data security,” Glob. J. Eng. Technol. Adv., vol. 18, no. 2, pp. 065–075, 2024, doi: 10.30574/gjeta.2024.18.2.0027.
[10] A. A. Khan, X. Zhang, F. Hajjej, J. Yang, C. S. Ku, and L. Y. Por, “ASMF: Ambient social media forensics chain of custody with an intelligent digital investigation process using federated learning,” Heliyon, vol. 10, no. 1, p. e23254, 2024, doi: 10.1016/j.heliyon.2023.e23254.
[11] H. Saini et al., “Hybrid Optimization Machine Learning Framework for Enhancing Trust and Security in Cloud Network,” IEEE Access, vol. 12, no. December, pp. 195943–195959, 2024, doi: 10.1109/ACCESS.2024.3520665.
[12] K. Yang and A. Yilmaz, “Crowd Scene Anomaly Detection in Online Videos,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 48-2–2024, no. June, pp. 443–448, 2024, doi: 10.5194/isprs-archives-XLVIII-2-2024-443-2024.
[13] Y. Wang et al., “A Survey on Metaverse: Fundamentals, Security, and Privacy,” IEEE Commun. Surv. Tutorials, vol. 25, no. 1, pp. 319–352, 2023, doi: 10.1109/COMST.2022.3202047.
[14] K. Li et al., “VideoChat: Chat-Centric Video Understanding,” pp. 1–16, 2023.
[15] Ehsan Bazgir, Ehteshamul Haque, Numair Bin Sharif, and Md. Faysal Ahmed, “Security aspects in IoT based cloud computing,” World J. Adv. Res. Rev., vol. 20, no. 3, pp. 540–551, 2023, doi: 10.30574/wjarr.2023.20.3.2481.
[16] S. Zhao, J. Miao, J. Zhao, and N. Naghshbandi, “A comprehensive and systematic review of the banking systems based on pay-as-you-go payment fashion and cloud computing in the pandemic era,” Inf. Syst. E-bus. Manag., no. 0123456789, 2023, doi: 10.1007/s10257-022-00617-9.
[17] T. Y. Wu, F. Kong, Q. Meng, S. Kumari, and C. M. Chen, “Rotating behind security: an enhanced authentication protocol for IoT-enabled devices in distributed cloud computing architecture,” Eurasip J. Wirel. Commun. Netw., vol. 2023, no. 1, 2023, doi: 10.1186/s13638-023-02245-4.
[18] P. Krishnan, K. Jain, A. Aldweesh, P. Prabu, and R. Buyya, “OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure,” J. Cloud Comput., vol. 12, no. 1, 2023, doi: 10.1186/s13677-023-00406-w.
[19] M. Suganya and T. Sasipraba, “Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment,” J. Cloud Comput., vol. 12, no. 1, 2023, doi: 10.1186/s13677-023-00442-6.
[20] A. R. Al-Ghuwairi, Y. Sharrab, D. Al-Fraihat, M. AlElaimat, A. Alsarhan, and A. Algarni, “Intrusion detection in cloud computing based on time series anomalies utilizing machine learning,” J. Cloud Comput., vol. 12, no. 1, 2023, doi: 10.1186/s13677-023-00491-x.
[21] J. Li, W. Xiao, and C. Zhang, “Data security crisis in universities: identification of key factors affecting data breach incidents,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, 2023, doi: 10.1057/s41599-023-01757-0.
[22] R. Bhat, N. R. Sunitha, and S. S. Iyengar, “A probabilistic public key encryption switching scheme for secure cloud storage,” Int. J. Inf. Technol., vol. 15, no. 2, pp. 675–690, 2023, doi: 10.1007/s41870-022-01084-8.
[23] S. Ahmadi, “Security And Privacy Challenges in Cloud-Based Data Warehousing: A Comprehensive Review,” Int. J. Comput. Sci. Trends Technol., vol. 11, pp. 17–27, 2023, [Online]. Available: www.ijcstjournal.org
[24] J. Wang, Y. Liu, S. Rao, R. S. Sherratt, and J. Hu, “Enhancing Security by Using GIFT and ECC Encryption Method in Multi-Tenant Datacenters,” Comput. Mater. Contin., vol. 75, no. 2, pp. 3849–3865, 2023, doi: 10.32604/cmc.2023.037150.
[25] S. Stewart Kirubakaran, V. P. Arunachalam, S. Karthik, and S. Kannan, “Towards Developing Privacy-Preserved Data Security Approach (PP-DSA) in Cloud Computing Environment,” Comput. Syst. Sci. Eng., vol. 44, no. 3, pp. 1881–1895, 2023, doi: 10.32604/csse.2023.026690.
[26] C. Shekhar Tiwari and V. Kumar Jha, “Enhancing the Cloud Security through RC6 and 3DES Algorithms while Achieving Low-Cost Encryption,” Int. J. Wirel. Microw. Technol., vol. 13, no. 5, pp. 48–59, 2023, doi: 10.5815/ijwmt.2023.05.05.
[27] R. R. Irshad et al., “IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Blockchain-Based Approach Toward a Trustworthy Cloud Computing,” IEEE Access, vol. 11, no. October, pp. 105479–105498, 2023, doi: 10.1109/ACCESS.2023.3318755.
[28] B. Rahul and K. Kuppusamy, “Efficiency Analysis of Cryptographic Algorithms for Image Data Security in Cloud Environment,” IETE J. Res., vol. 69, no. 9, pp. 6053–6064, 2023, doi: 10.1080/03772063.2021.1990141.
[29] M. N. Katsantonis, A. Manikas, I. Mavridis, and D. Gritzalis, “Cyber range design framework for cyber security education and training,” Int. J. Inf. Secur., vol. 22, no. 4, pp. 1005–1027, 2023, doi: 10.1007/s10207-023-00680-4.
[30] D. K. Murala, S. K. Panda, and S. K. Sahoo, Securing Electronic Health Record System in Cloud Environment Using Blockchain Technology, vol. 237, no. February. Springer International Publishing, 2023. doi: 10.1007/978-3-031-22835-3_4.
[31] N. Gaur and S. Singh, “A Behaviour Study on Cloud Eco-System: Data Security Perspective,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 6, pp. 172–177, 2023, doi: 10.17762/ijritcc.v11i6.7379.
[32] H. Attou, A. Guezzaz, S. Benkirane, M. Azrour, and Y. Farhaoui, “Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques,” Big Data Min. Anal., vol. 6, no. 3, pp. 311–320, 2023, doi: 10.26599/BDMA.2022.9020038.
[33] S. S. Pericherla, “Cloud Computing Threats, Vulnerabilities and Countermeasures: A State-of-the-Art,” ISeCure, vol. 15, no. 1, pp. 55–112, 2023, doi: 10.22042/isecure.2022.312328.718.
[34] M. Z. Hasan, M. Z. Hussain, Z. Mubarak, A. A. Siddiqui, A. M. Qureshi, and I. Ismail, “Data security and Integrity in Cloud Computing,” 2023 Int. Conf. Adv. Technol. ICONAT 2023, no. April, 2023, doi: 10.1109/ICONAT57137.2023.10080440.
[35] P. Maniatis, “Comparison of Public, Private, Hybrid, and Community Cloud Computing in Terms of Purchasing and Supply Management: A Quantitative Approach,” Int. J. Multidiscip. Res. Anal., vol. 06, no. 06, pp. 2359–2369, 2023, doi: 10.47191/ijmra/v6-i6-04.
[36] D. Kumar Sharma, D. Sreenivasa Chakravarthi, A. Ara Shaikh, A. Al Ayub Ahmed, S. Jaiswal, and M. Naved, “The aspect of vast data management problem in healthcare sector and implementation of cloud computing technique,” Mater. Today Proc., vol. 80, no. September, pp. 3805–3810, 2023, doi: 10.1016/j.matpr.2021.07.388.
[37] V. V Vegesna, “The Utilization of Information Systems for Supply Chain Management for Multicomponent Productivity Based on Cloud Computing,” Int. J. Manag. Technol. …, no. October, 2023, [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4612525%0Ahttps://www.researchgate.net/profile/Vinod-Varma-Vegesna/publication/374949401_The_Utilization_of_Information_Systems_for_Supply_Chain_Management_for_Multicomponent_Productivity_Based_on_Cloud_
[38] V. Varma Vegesna, “A Comprehensive Investigation of Privacy Concerns in the Context of Cloud Computing Using Self-Service Paradigms,” no. October, 2023, [Online]. Available: https://ssrn.com/abstract=4612536
[39] D. Selvaraj, S. M. U. Sankar, D. Dhinakaran, and T. P. Anish, “Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection,” SSRG Int. J. Electr. Electron. Eng., vol. 10, no. 1, pp. 53–62, 2023, doi: 10.14445/23488379/IJEEE-V10I1P105.
[40] A. Pakmehr, A. Aßmuth, C. P. Neumann, and G. Pirkl, “Security Challenges for Cloud or Fog Computing-Based AI Applications,” pp. 21–29, 2023, [Online]. Available: http://arxiv.org/abs/2310.19459
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