Jihoon Shin

Jihoon Shin

he/his

Project Specialist at Syracuse University with 7 years of experience in IT systems management and 2 years of experience in machine learning research.

Posts by Collection

portfolio

publications

Towards Building Generalizable Models for Malware Detection

Published in 2024 IEEE International Conference on Big Data (BigData), 2024

Recommended citation: Shin, J., Rivas, E., Lucio, D., Piplai, A., & Elluri, L. (2024, December). Towards Building Generalizable Models for Malware Detection. In 2024 IEEE International Conference on Big Data (BigData) (pp. 5656–5663). IEEE.
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Understanding Artificial Intelligence and Machine Learning: Foundational Concepts for Counselor Education

Published in Journal of Counselor Preparation and Supervision, 2025

ACES Recognition (December 23, 2025)
Recognized by the Association for Counselor Education and Supervision (ACES) as one of the most accessed articles of 2025 (ACES newsletter feature). This publication introduces foundational concepts of artificial intelligence and machine learning for counselor education.

Recommended citation: Han, E. & Shin, J. (2025). Understanding Artificial Intelligence and Machine Learning: Foundational Concepts for Counselor Education. Journal of Counselor Preparation and Supervision, 19(3), 1-15. http://dx.doi.org/10.70013/7b5l47yn
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Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents

Published in arXiv preprint (Computer Science > Cryptography and Security), 2026

Recommended citation: Lukáš, O., Shin, J., Rivas, E., Forni, D., Rigaki, M., Catania, C., Piplai, A., Kiekintveld, C., & Garcia, S. (2026). Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents. arXiv preprint arXiv:2603.10041. https://doi.org/10.48550/arXiv.2603.10041
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talks

Towards Building Generalizable Models for Malware Detection

Published:

Recorded presentation of our accepted paper at the BigCyber-2024 Workshop, part of the 2024 IEEE International Conference on Big Data.
The talk highlights our use of meta-learning (MAML) to develop models that generalize across unseen malware families using diverse datasets such as BODMAS, Avast-CTU, and MaleVis.

Towards Building Generalizable Models for Malware Detection

Published:

Presented a poster on using meta-learning techniques to improve malware detection across unseen malware families. The project integrates Model-Agnostic Meta-Learning (MAML) with datasets like BODMAS, MaleVis, and Avast-CTU to enhance model generalization in cybersecurity contexts.

Building Generalizable Learning Systems for Dynamic Cybersecurity Environments

Published:

Presented as a Master’s thesis defense for the M.S. in Computer Science. This presentation examined generalizable learning systems for dynamic cybersecurity environments using meta-learning and reinforcement learning, with applications to malware detection and network security simulation.

teaching