1. Jasper Gnana Chandran, J., Karthick, R., Rajagopal, R., & Meenalochini, P. (2023). Dual-channel capsule generative adversarial network optimized with golden eagle optimization for pediatric bone age assessment from hand X-ray image. International Journal of Pattern Recognition and Artificial Intelligence, 37(02), 2354001.
2. Karthick, R., Prabha, M., Sabapathy, S. R., Jiju, D., & Selvan, R. S. (2023, October). Inspired by social-spider behavior for microwave filter optimization, swarm optimization algorithm. In 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) (Vol. 1, pp. 1-4). IEEE.
3. Vijayalakshmi, S., Sivaraman, P. R., Karthick, R., & Ali, A. N. (2020, September). Implementation of a new Bi-Directional Switch multilevel Inverter for the reduction of harmonics. In IOP Conference Series: Materials Science and Engineering (Vol. 937, No. 1, p. 012026). IOP Publishing.
4. Kiruthiga, B., Karthick, R., Manju, I., & Kondreddi, K. (2024). Optimizing harmonic mitigation for smooth integration of renewable energy: A novel approach using atomic orbital search and feedback artificial tree control. Protection and Control of Modern Power Systems, 9(4), 160-176.
5. Sulthan Alikhan, J., Miruna Joe Amali, S., & Karthick, R. (2024). Deep Siamese domain adaptation convolutional neural network-based quaternion fractional order Meixner moments fostered big data analytical method for enhancing cloud data security. Network: Computation in Neural Systems, 1-28.
6. Sakthi, P., Bhavani, R., Arulselvam, D., Karthick, R., Selvakumar, S., & Sudhakar, M. (2022, September). Energy efficient cluster head selection and routing protocol for WSN. In AIP Conference Proceedings (Vol. 2518, No. 1). AIP Publishing.
7. Aravindaguru, I., Arulselvam, D., Kanagavalli, N., Ramkumar, V., & Karthick, R. (2022, September). Space cloud in cubesat-Consigning expert system to space. In AIP Conference Proceedings (Vol. 2518, No. 1). AIP Publishing.
8. Karthick, R., Prabaharan, A. M., & Selvaprasanth, P. (2019). A Dumb-Bell shaped damper with magnetic absorber using ferrofluids. International Journal of Recent Technology and Engineering (IJRTE), 8.
9. Selvan, R. S., Wahidabanu, R. S. D., Karthick, B., Sriram, M., & Karthick, R. (2020). Development of Secure Transport System Using VANET. TEM (H-Index), 82.
10. Karthick, R., & Sundararajan, M. (2018). Optimization of MIMO Channels Using an Adaptive LPC Method. International Journal of Pure and Applied Mathematics, 118(10), 131-135.
11. Lopez, S., Sarada, V., Praveen, R. V. S., Pandey, A., Khuntia, M., & Haralayya, D. B. (2024). Artificial intelligence challenges and role for sustainable education in india: Problems and prospects. Sandeep Lopez, Vani Sarada, RVS Praveen, Anita Pandey, Monalisa Khuntia, Bhadrappa Haralayya (2024) Artificial Intelligence Challenges and Role for Sustainable Education in India: Problems and Prospects. Library Progress International, 44(3), 18261-18271.
12. Kumar, N., Kurkute, S. L., Kalpana, V., Karuppannan, A., Praveen, R. V. S., & Mishra, S. (2024, August). Modelling and Evaluation of Li-ion Battery Performance Based on the Electric Vehicle Tiled Tests using Kalman Filter-GBDT Approach. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
13. Sharma, S., Vij, S., Praveen, R. V. S., Srinivasan, S., Yadav, D. K., & VS, R. K. (2024, October). Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1631-1636). IEEE.
14. Yamuna, V., Praveen, R. V. S., Sathya, R., Dhivva, M., Lidiya, R., & Sowmiya, P. (2024, October). Integrating AI for Improved Brain Tumor Detection and Classification. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1603-1609). IEEE.
15. Anuprathibha, T., Praveen, R. V. S., Jayanth, H., Sukumar, P., Suganthi, G., & Ravichandran, T. (2024, October). Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings. In 2024 Global Conference on Communications and Information Technologies (GCCIT) (pp. 1-5). IEEE.
Page No: 202 - 209
Abstract : The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and micro-segmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (AI), machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape.
Keyword Zero Trust Architecture, Cybersecurity Framework, Identity Verification, Least Privilege Access, AI in Security, Threat Detection, Cloud Security, Micro-Segmentation, Zero Trust Networks, Risk Mitigation.
Reference: