Articles Open Access
Secure Remote Work Infrastructure Using Zero Trust Cloud Security Architecture Models
UBDT College of Engineering, Davanagere, India
Abstract
The rapid adoption of remote work environments has significantly increased the demand for secure, scalable, and adaptive cybersecurity frameworks. Traditional perimeter-based security models are no longer sufficient to protect distributed cloud-based infrastructures, where users access enterprise resources from multiple devices and locations. Zero Trust Security Architecture (ZTSA) has emerged as a modern cybersecurity paradigm that operates on the principle of “never trust, always verify.” This model continuously authenticates and authorizes every user, device, and network interaction regardless of location. In secure remote work infrastructures, Zero Trust integrates identity management, multi-factor authentication, micro-segmentation, least privilege access, and real-time monitoring to minimize security risks. Cloud computing further enhances scalability and accessibility, enabling organizations to support distributed workforces securely. Zero Trust cloud security models help prevent unauthorized access, data breaches, insider threats, and ransomware attacks. Advanced technologies such as artificial intelligence, behavioral analytics, and encryption strengthen the effectiveness of Zero Trust frameworks. However, challenges such as implementation complexity, high deployment costs, integration issues, and user experience limitations remain significant. This study explores Zero Trust cloud security architecture for remote work environments, reviews relevant literature, examines research methodologies, and evaluates its advantages and disadvantages in modern cybersecurity systems.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 4 |
| Number | 3 |
| Pages | 6131-6138 |
| Published | 2020-05-05 |
| Issue | Vol. 4 No. 3 (2020): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Kindervag, J. (2010). Build Security Into Your Network’s DNA: The Zero Trust Model. Forrester Research.
2. Gilman, E., & Barth, D. (2017). Zero Trust Networks. O’Reilly Media.
3. Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583–592.
4. Subashini, S., & Kavitha, V. (2011). A survey on security issues in cloud computing. Journal of Network and Computer Applications, 34(1), 1–11.
5. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. NIST.
6. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.
7. Chen, D., & Zhao, H. (2012). Data security and privacy protection issues in cloud computing. International Conference on Computer Science and Electronics Engineering.
8. Takabi, H., Joshi, J. B. D., & Ahn, G. J. (2010). Security and privacy challenges in cloud computing environments. IEEE Security & Privacy, 8(6), 24–31.
9. Pearson, S. (2013). Privacy, security and trust in cloud computing. Cloud Security Handbook.
10. Armbrust, M., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
11. ENISA (2013). Cloud Computing: Benefits, risks and recommendations. European Union Agency for Cybersecurity.
12. ENISA (2016). Secure use of cloud computing in the enterprise.
13. Amazon Web Services (2018). AWS Security Best Practices Whitepaper.
14. Microsoft (2018). Zero Trust Security Model Overview. Microsoft Security Documentation.
15. Google (2017). BeyondCorp: A New Approach to Enterprise Security. Google Whitepaper.
16. Somani, U., et al. (2017). Cloud computing security: Challenges and solutions. Procedia Computer Science, 85, 133–141.
17. Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernandez, E. B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4(1), 1–13.
18. Krutz, R. L., & Vines, R. D. (2010). Cloud Security: A Comprehensive Guide to Secure Cloud Computing. Wiley.
19. Chen, Y., Paxson, V., & Katz, R. H. (2010). What’s new about cloud computing security? University of California Berkeley Technical Report.
20. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
21. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
22. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
23. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
24. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
25. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
26. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
27. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
28. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
29. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
30. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
31. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
32. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
33. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
34. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
35. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
36. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
37. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
38. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
39. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
40. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
41. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
42. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
43. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
44. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
45. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
2. Gilman, E., & Barth, D. (2017). Zero Trust Networks. O’Reilly Media.
3. Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583–592.
4. Subashini, S., & Kavitha, V. (2011). A survey on security issues in cloud computing. Journal of Network and Computer Applications, 34(1), 1–11.
5. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. NIST.
6. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.
7. Chen, D., & Zhao, H. (2012). Data security and privacy protection issues in cloud computing. International Conference on Computer Science and Electronics Engineering.
8. Takabi, H., Joshi, J. B. D., & Ahn, G. J. (2010). Security and privacy challenges in cloud computing environments. IEEE Security & Privacy, 8(6), 24–31.
9. Pearson, S. (2013). Privacy, security and trust in cloud computing. Cloud Security Handbook.
10. Armbrust, M., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
11. ENISA (2013). Cloud Computing: Benefits, risks and recommendations. European Union Agency for Cybersecurity.
12. ENISA (2016). Secure use of cloud computing in the enterprise.
13. Amazon Web Services (2018). AWS Security Best Practices Whitepaper.
14. Microsoft (2018). Zero Trust Security Model Overview. Microsoft Security Documentation.
15. Google (2017). BeyondCorp: A New Approach to Enterprise Security. Google Whitepaper.
16. Somani, U., et al. (2017). Cloud computing security: Challenges and solutions. Procedia Computer Science, 85, 133–141.
17. Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernandez, E. B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4(1), 1–13.
18. Krutz, R. L., & Vines, R. D. (2010). Cloud Security: A Comprehensive Guide to Secure Cloud Computing. Wiley.
19. Chen, Y., Paxson, V., & Katz, R. H. (2010). What’s new about cloud computing security? University of California Berkeley Technical Report.
20. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
21. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
22. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
23. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
24. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
25. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
26. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
27. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
28. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
29. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
30. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
31. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
32. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
33. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
34. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
35. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
36. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
37. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
38. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
39. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
40. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
41. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
42. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
43. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
44. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
45. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
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