Daemin Shin | Computer Science | Academic Luminary Achievement Award

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Dr. Daemin Shin | Computer Science | Academic Luminary Achievement Award

Manager at Financial Security Institute (FSI), South Korea

Daemin Shin is a Manager at the Financial Security Institute, where he has been actively involved in advancing financial security measures since April 2015. With expertise in cloud security, Zero Trust security models, and data security, he has played a significant role in shaping secure financial infrastructures. Before his current role, he was a Senior Researcher at the Financial Security Research Institute from July 2012 to April 2015. His contributions to financial cybersecurity research have been instrumental in addressing security threats and enhancing the resilience of financial institutions. Shin continues to lead innovative research and development in financial security.

Profile

Scopus

Education

Daemin Shin earned his Master of Science in Engineering from the Graduate School of Information Security at Korea University, South Korea, in February 2009. He further pursued his Ph.D. in Engineering at the Department of Information Security, Soonchunhyang University, South Korea, which he successfully completed in February 2020. His academic journey reflects a strong foundation in cybersecurity, particularly focusing on financial security, cloud computing, and data protection. Throughout his education, he has been deeply engaged in research on securing financial transactions and developing security frameworks for modern digital finance ecosystems.

Experience

Shin has over a decade of experience in the field of financial security, with a strong emphasis on cloud security, data protection, and Zero Trust architectures. He started his career as a Senior Researcher at the Financial Security Research Institute, where he contributed to innovative research projects on financial cybersecurity from 2012 to 2015. Since April 2015, he has been serving as a Manager at the Financial Security Institute, where he continues to work on financial security infrastructure, cybersecurity policies, and security compliance strategies. His professional experience has significantly contributed to the development of robust security measures for the financial sector.

Research Interests

Shin’s research interests primarily focus on cloud security, financial security, and Zero Trust security models. He has conducted extensive research on securing cloud-based financial infrastructures, ensuring compliance with regulatory requirements, and mitigating security threats in digital finance. His recent works include studies on security considerations for DevSecOps software supply chains and Zero Trust evaluation frameworks tailored for financial institutions. His expertise in these domains has positioned him as a thought leader in enhancing cybersecurity resilience in the financial industry.

Awards and Recognitions

Shin has been recognized for his outstanding contributions to financial security and cybersecurity research. He has been nominated for the Best Researcher Award in recognition of his groundbreaking research on cloud security and financial security frameworks. His efforts in improving security compliance policies and implementing Zero Trust methodologies in financial institutions have gained widespread recognition. Shin’s work has had a substantial impact on the cybersecurity domain, making financial transactions and data storage more secure against emerging threats.

Publications

D. Shin, V. Sharma, J. Kim, S. Kwon, and I. You (2017). “Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks,” IEEE Access, vol. 5, pp. 11100-11117, DOI: 10.1109/ACCESS.2017.2710379. Cited by 200+ articles.

D. Shin, K. Yun, J. Kim, P. V. Astillo, J.-N. Kim, and I. You (2019). “A Security Protocol for Route Optimization in DMM-Based Smart Home IoT Networks,” IEEE Access, vol. 7, pp. 142531-142550, DOI: 10.1109/ACCESS.2019.2943929. Cited by 150+ articles.

Shin, Daemin, Kim, Jiyoon, & You, Ilsun (2023). “국내 금융구득 클라우드 전환 동형 및 보안,” REVIEW OF KIISC, 33(5), 57-68. Cited by 50+ articles.

Shin, Daemin, You, Ilsun, and Kim, Jiyoon (2024). “국내 금융구득 클라우드 보안 위험 및 보안 요구사항에 관한 연구,” Journal of Next-Generation Computing, 20(4), 77-96, DOI: 10.23019/kingpc.20.4.202408.007. Cited by 30+ articles.

Daemin Shin, Jiyoon Kim, I Wayan Adi Juliawan Pawana, Ilsun You (2025). “Enhancing Cloud-Native DevSecOps: A Zero Trust Approach for the Financial Sector,” Computer Standards & Interfaces, DOI: 10.1016/j.csi.2025.103975. Cited by 20+ articles.

Conclusion

Daemin Shin’s dedication to advancing financial security and cybersecurity has been instrumental in shaping modern security frameworks for financial institutions. His research on cloud security, Zero Trust models, and DevSecOps methodologies continues to drive innovation in securing financial infrastructures. With a strong academic and professional background, he remains committed to developing secure financial ecosystems and mitigating cybersecurity risks in an ever-evolving digital landscape. His contributions have earned him significant recognition, making him a leading figure in financial security research.

Muhammad Dilshad | Data Privacy and Security | AI & Machine Learning Award

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Mr. Muhammad Dilshad | Data Privacy and Security | AI & Machine Learning Award

Student at Quaid e Azam University Islamabad, Pakistan

Muhammad Dilshad is a dedicated and driven professional in the field of Computer and Information Technology. Holding a Master’s degree in Computer and Information Technology (MCIT) from Quaid-i-Azam University, Islamabad, he specializes in Cybersecurity, Networking, Machine Learning, and Blockchain. With practical experience in network performance monitoring and troubleshooting, he has contributed significantly to optimizing infrastructure security. His research interests revolve around enhancing Internet of Vehicles (IoV) security, employing Federated Learning, and integrating Blockchain technology to build decentralized, tamper-resistant frameworks. Proficient in various programming languages and analytical tools, he continually strives to apply emerging technologies for solving real-world security challenges.

Profile

Orcid

Education

Muhammad Dilshad began his academic journey with a strong foundation in science and mathematics, completing his Matriculation from BISE DG Khan Board. He then pursued an Intermediate of Computer Science (ICS) from the same board, gaining expertise in programming and computational concepts. His passion for technology led him to obtain a Bachelor of Science in Information Technology (BSIT) from Bahauddin Zakariya University, Multan, where he honed his skills in web development, networking, and database management. He further advanced his knowledge by earning a Master of Science in Information Technology (MSIT) from Quaid-i-Azam University, Islamabad, specializing in Machine Learning, Federated Learning, Blockchain, and Cybersecurity. His academic excellence is reflected in his impressive CGPAs and his continuous learning through various certifications.

Work Experience

Muhammad Dilshad has amassed valuable hands-on experience through his roles at Pakistan Telecommunication Company Limited (PTCL). He completed an internship at PTCL, where he actively monitored network performance, troubleshot connectivity issues, and assisted in optimizing infrastructure using tools like SolarWinds and CRM. He later transitioned into a Technical Support Associate (TSA) role in PTCL’s USD department, where he provided technical support, resolved network issues, and maintained high customer satisfaction ratings. His work has significantly contributed to improving service reliability and network security within the organization.

Research Interest

With a keen interest in cybersecurity, networking, and advanced computing paradigms, Muhammad Dilshad focuses his research on enhancing security frameworks for the Internet of Vehicles (IoV). His work primarily involves using Machine Learning techniques for DDoS attack detection and employing Federated Learning to create more secure, decentralized architectures. His expertise in Blockchain technology enables him to develop tamper-resistant security frameworks that protect critical data integrity. Additionally, he is passionate about applying Data Science methodologies for predictive analytics, improving network security, and optimizing intelligent systems. His research contributions aim to address contemporary challenges in network security and privacy, with a focus on real-world implementations.

Awards

Muhammad Dilshad has been recognized for his outstanding contributions to the field of Information Technology. His innovative research on IoV security and Blockchain applications has earned him nominations for prestigious awards in academia and industry. His work has been appreciated at international conferences, and he has received accolades for his impactful presentations on cybersecurity and emerging technologies. He continues to seek new opportunities to contribute to the scientific community and enhance technological advancements in cybersecurity and networking.

Publications

IOV Cyber Defense: Advancing DDoS Attack Detection with Gini Index in Tree Models (2024) – Published in a reputed journal, this paper explores the effectiveness of tree-based models in detecting cyber threats in IoV environments. Cited by multiple cybersecurity research articles.

Blockchain-Enabled Secure and Efficient DDoS Attack Detection Mechanisms in Connected Internet of Vehicles Using Federated Learning (2024) – Accepted at the 21st International Conference on Frontiers of Information Technology (FIT 2024). Recognized for innovative integration of Blockchain and Federated Learning.

Efficient DDoS Attack Detection in the Internet of Vehicles Using Gini Index and Federated Learning (2024) – Submitted to MDPI Journal, this paper proposes an advanced security mechanism for IoV systems. Highly relevant for researchers in cybersecurity.

Conclusion

Muhammad Dilshad’s dedication to advancing the fields of cybersecurity, networking, and artificial intelligence is evident in his extensive research and professional experience. His expertise in Machine Learning, Blockchain, and Federated Learning continues to contribute significantly to the development of secure, decentralized systems. Through his work at PTCL and his academic pursuits, he has demonstrated a strong commitment to innovation and problem-solving. With a growing list of publications, awards, and research contributions, he remains at the forefront of technological advancements, striving to make impactful changes in network security and intelligent systems.