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- [Apr 09, 2026] Dr. Hyunmin Choi Appointed at Dankook University (Security Laboratory, Advisor: Professor Hyoungshick Kim NEW
- Dr. Hyunmin Choi, an alumnus of the Security Laboratory at Sungkyunkwan University (Advisor: Professor Hyoungshick Kim), has been appointed as a full-time faculty member in the Department of Software at Dankook University as of March 2026. Professor Hyunmin Choi previously worked as a researcher at Naver Cloud and later completed his Ph.D. in the Security Laboratory at Sungkyunkwan University, earning his degree in August 2025. During his doctoral studies, Professor Choi conducted research in homomorphic encryption, privacy-preserving technologies, and AI security. In particular, he focused on privacy-enhancing techniques that enable the secure use of sensitive data, as well as improving the security and trustworthiness of artificial intelligence systems. Based on this research background, Professor Choi is expected to continue active research and teaching in various areas where security and artificial intelligence converge.
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- 작성일 2026-04-14
- 조회수 40
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- [Apr 09, 2026] Dr. Beomjin Jin Appointed at Ajou University (Security Laboratory, Advisor: Professor Hyoungshick Kim) NEW
- Dr. Beomjin Jin, an alumnus of the Security Laboratory at Sungkyunkwan University (Advisor: Professor Hyoungshick Kim), has been appointed as a full-time faculty member in the Department of Cybersecurity at Ajou University as of March 2026. Professor Beomjin Jin received his bachelor’s degree from the Department of Computer Science and Engineering at Sungkyunkwan University, and completed his integrated M.S.-Ph.D. program in the Department of Electrical and Computer Engineering at Sungkyunkwan University under the supervision of Professor Hyoungshick Kim, earning his Ph.D. in August 2025. He then continued his research in cybersecurity as a postdoctoral researcher in the Department of Computer Science at Purdue University in the United States. During his academic training, Professor Beomjin Jin conducted research primarily in malware analysis, cyber threat intelligence, and software security. In particular, he focused on analyzing real-world security threats in depth—such as malware rule generation and variant analysis, cyber threat intelligence analysis, and privacy protection in the web ecosystem—and connecting these insights to effective countermeasure technologies. More recently, he has also been interested in automating malware analysis and threat intelligence generation using artificial intelligence and large language models, and is expected to contribute to the development of more effective and scalable cyber threat response technologies.
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- 작성일 2026-04-14
- 조회수 32
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- [Apr 09, 2026] Security Laboratory (Advised by Professor Hyoungshick Kim) Has Two Papers Accepted to CHI 2026 NEW
- Security Laboratory, Two Papers Accepted to CHI 2026 Empirical Analysis of the Limits of User Awareness and Response Behavior After Large-Scale Security Incidents Two research papers from the Security Laboratory at Sungkyunkwan University (Advisor: Professor Hyoungshick Kim) have been accepted to CHI 2026, the most prestigious international conference in the field of HCI. This achievement is significant in that it empirically analyzes how users understand risks and what response behaviors they actually exhibit following large-scale security incidents. One study, conducted in collaboration with KAIST, examines a major SIM data breach in South Korea, while the other, conducted with Georgia Tech and Samsung Research, analyzes changes in cryptocurrency users’ security perceptions and response patterns following the collapse of FTX. Both studies move beyond focusing solely on technical vulnerabilities and instead examine user perception, trust, and behavior, highlighting the importance of user-centered security design. The first paper, "Mind the SIM: Awareness and Mental Models in a South Korean Case Study,” investigates how users understand risks related to SIM-based authentication in the context of a large-scale SIM authentication data breach in South Korea in 2025. Through interviews and mental model analysis with 33 participants, the research team found that while many users were aware that the incident had occurred, they did not clearly understand what information had been leaked or the potential risks it could lead to. In particular, users tended to vaguely perceive the severity of the incident while underestimating their own risk, or assumed that it was the responsibility of telecom providers, leading to a lack of proactive response. This demonstrates that the often-cited “gap between awareness and action” in security incidents clearly exists in the domain of telecommunications authentication infrastructure as well. Based on these findings, the researchers suggest that future telecommunications security services and authentication systems should be designed not only for technical robustness but also with explanatory frameworks and guidance structures that help users accurately understand risks and translate that understanding into protective actions. The second paper, “I just have faith in my wallet to not mismanage my crypto”: Investigating Changes in Users’ Security Perceptions Post-FTX Collapse,” examines how cryptocurrency users perceive the security of custodial versus non-custodial wallets after the collapse of FTX, and whether these perception changes lead to actual behavioral responses. Based on 22 in-depth interviews and a follow-up survey of 430 participants, the study found that trust in centralized exchanges generally declined after the incident, while self-managed wallets were perceived as more secure. However, these shifts in perception did not consistently translate into action. Many users continued to keep their assets in existing services, and a considerable number did not fully understand the fundamental structure in which exchanges hold their private keys. Notably, some users recognized the risks but took no action, while others believed they were using safer methods but in reality remained within risky structures. The researchers emphasize that security communication in cryptocurrency services should go beyond simply providing information and instead help users concretely assess their risks and take immediate action. The acceptance of these papers to CHI 2026 demonstrates that the Security Laboratory has established international competitiveness not only in technology-driven security research but also in human-centered security research that closely examines real user experiences and behaviors. Although the two studies focus on different domains, both empirically confirm that even after large-scale incidents, users repeatedly exhibit limited understanding, incomplete mental models, and delayed responses. This suggests that future security technologies should not only aim to build more secure systems but also evolve to support users in understanding risks and responding appropriately. The findings of both studies will be presented on April 14 (local time) at ACM CHI 2026 in Barcelona.
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- 작성일 2026-04-14
- 조회수 32
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- [Apr 07, 2026] Professor Sooyoung Cha’s Software Analysis Laboratory (SAL) Has a Paper Accepted to FSE 2026 NEW
- The paper by Jaehan Yoon (Ph.D. student) from the Software Analysis Laboratory (Advisor: Sooyoung Cha) has been accepted to FSE 2026 (ACM International Conference on the Foundations of Software Engineering), the top conference in the field of software engineering. This research was conducted in collaboration with Yunji Seo (Korea University) and Professor Hakjoo Oh (Korea University), and will be presented in Montreal, Canada in July 2026. The paper, "Reducing Coverage-Equivalent Inputs in Grammar-based Fuzzing by Avoiding Recurrent Rule Sequences,” proposes a new technique to improve the performance of “Grammar-based Fuzzing,” a representative software testing methodology. The motivation of this work is that recent grammar-based fuzzing tools repeatedly generate coverage-equivalent inputs that execute the same code regions during testing. To address this issue, the paper introduces a technique that automatically identifies the causes of such coverage-equivalent inputs and generates “production rules” to prevent their regeneration. The proposed approach, RSFuzz, was integrated into state-of-the-art fuzzing tools and evaluated on 12 real-world programs with various input formats. Experimental results show that integrating RSFuzz into three fuzzing tools detects 121, 46, and 17 additional bugs, improves line coverage by 6.0%, 4.8%, and 3.0%, and reduces the generation of coverage-equivalent inputs by 23.3%, 28.7%, and 14.9%, respectively. [Paper Information] - Title: Reducing Coverage-Equivalent Inputs in Grammar-based Fuzzing by Avoiding Recurrent Rule Sequences - Authors: Jaehan Yoon, Yunji Seo, Hakjoo Oh, Sooyoung Cha - Conference: ACM International Conference on the Foundations of Software Engineering (FSE 2026) Abstract: We present RSFuzz, a new technique to enhance grammar-based fuzzing by reducing the generation of coverage-equivalent inputs during testing. Grammar-based fuzzers apply production rules from a given grammar (e.g., forming a derivation tree) to generate well-structured inputs for the target program. However, a key limitation is that many existing fuzzers still produce a large number of "coverage-equivalent" inputs—those that revisit already explored program paths—thereby restricting their ability to uncover new bugs and improve coverage. To address this issue, RSFuzz automatically identifies recurrent sequences of production rules that lead to coverage-equivalent inputs and prevents their reuse during fuzzing. A key challenge in practice lies in the large number of coverage-equivalent input groups, each with many inputs and corresponding derivation trees, making it difficult to identify the underlying recurrent sequences. RSFuzz tackles this challenge with a customized algorithm that iteratively groups coverage-equivalent inputs, selects promising groups, and extracts recurrent sequences by abstracting derivation trees based on accumulated data while running any grammar-based fuzzer. We integrated RSFuzz with existing random, probabilistic, and grammar-coverage based fuzzers and evaluated it on 12 real-world programs using XML, JSON, CSV, and Markdown input formats. Experimental results show that incorporating RSFuzz into the three fuzzers detects 121, 46, and 17 additional crashes with distinct stack traces, increases line coverage by 6.0%, 4.8%, and 3.0%, and reduces duplicate-coverage input generation by 23.3%, 28.7% and 14.9%, respectively, compared to their performance without RSFuzz.
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- 작성일 2026-04-14
- 조회수 32
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- [Mar 31, 2026] Dr. Sungmin Park Joins Hankuk University of Foreign Studies as Full-Time Faculty Member
- Dr. Sungmin Park, a graduate of our university’s Department of Artificial Intelligence (Advisor: JONG WUK, LEE), has been appointed as a full-time faculty member in the Division of AI Data Convergence at Hankuk University of Foreign Studies, effective March 2026. Dr. Sungmin Park received his Ph.D. in February 2025 with a dissertation titled "Improving Linear Item-Item Recommender Models for Data Bias, Semantics, and Temporality." During his studies, Dr. Park conducted in-depth research in recommender systems, data mining, and natural language processing. He achieved outstanding, world-class research performance by publishing a total of 12 papers at top-tier international AI conferences such as SIGIR and KDD. In particular, his work on mitigating data bias and optimizing linear recommender models by incorporating temporal characteristics has attracted significant attention from the academic community. Recently, Dr. Park has been focusing on advancing recommender systems using large language models and multimodal data. At Hankuk University of Foreign Studies, he plans to continue innovative research that integrates AI and data technologies to address complex real-world problems. We extend our warmest congratulations to Dr. Sungmin Park on this new journey and wish him continued success in his future research and endeavors. Personal website: https://psm1206.github.io/
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- 작성일 2026-03-31
- 조회수 596
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- [Mar 31, 2026] Student Success Story : From an Unexpected Beginning to the Path of a Researcher - Jin kwon Lee (M.S.)
- The various experiences one can have at university can sometimes change the course of one’s life. This is because, through classes, clubs, and academic communities, many students discover an interest in fields they had never previously considered. Lee Jin-kwon, a graduate student who completed his undergraduate studies in the Department of Chinese Literature at our university, is one such example. He first encountered AI by chance through a club activity, and that experience eventually led him to pursue it as his field of study in his master’s program. In addition, he has demonstrated outstanding achievements in various academic societies and competitions, including winning the Excellence Award by placing third out of 52 teams in the 2025 Smart Agriculture Artificial Intelligence Competition. We had the opportunity to meet Lee Jin-kwon, who found a field that truly interested him and is now studying his chosen major in depth, and hear his story firsthand.
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- 작성일 2026-03-31
- 조회수 112
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- [Mar 27, 2026] AI Technology for Recognizing Actions Using Only a Few Example Videos
- A research team led by Jae-Pil Heo, Professor in the Department of Software at Sungkyunkwan University, has developed an Artificial Intelligence (AI) technology that can accurately recognize new actions from only a small number of example videos. Typically, AI requires massive amounts of training data to understand complex human actions. However, in real-world scenarios, it is often difficult to secure sufficient video data for specific actions. To address this limitation, the research team focused on few-shot action recognition, which enables AI to learn and distinguish the characteristics of new actions from only a few examples. The research team’s core idea is to compare videos by efficiently summarizing only their key movements, rather than relying on conventional complex computations that compare entire videos frame by frame in temporal order. To achieve this, the team extracts and organizes key movement patterns from each video based on several criteria, enabling the AI to compare actions more effectively and identify similarities and differences more accurately. A key strength of this technology is its robustness to variations in action speed and duration. Even when the same action is performed at different speeds or over different durations due to individual habits or filming conditions, the algorithm can reliably capture the essence of the action and recognize it effectively despite such temporal variations. This achievement has been internationally recognized for its academic significance and technical excellence. The paper was selected for an Oral Presentation at CVPR 2025, one of the most prestigious conferences in computer vision and artificial intelligence. This technology is expected to play an important role in a wide range of applications that require advanced video understanding, including sports motion analysis, intelligent security systems for detecting dangerous situations, and autonomous behavior learning for robots. ※Title: Temporal Alignment-Free Video Matching for Few-shot Action Recognition ※Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025 ※Presentation Type: Oral Presentation ※DOI: 10.1109/CVPR52734.2025.00509 ※Author: SuBeen Lee, WonJun Moon, Hyun Seok Seong, Jae-Pil Heo ※PURE: https://pure.skku.edu/en/persons/jae-pil-heo/
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- 작성일 2026-03-27
- 조회수 203
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- [Mar 16, 2026] Sangyeop Lee, PhD(Advisor Simon Sungil Woo), appointed assistant professor at Incheon National University
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Sangyeop Lee, PhD(Advisor: Simon Sungil Woo) of the College of Computing and Informatics(Department of Computer Science and Engineering), appointed assistant professor at Incheon National University
□ Sangyeop Lee, PhD of the Department of Computer Science and Engineering has been appointed as an Assistant Professor in the Department of Computer Engineering at Incheon National University as of March 2026. □ Sangyeop Lee, PhD received his doctoral degree in 2023 from the Department of Computer Science and Engineering in the College of Computing and Informatics at Sungkyunkwan University. During his doctoral studies, he conducted active research in deepfake detection, anomaly detection, and cybersecurity, publishing numerous papers at leading international conferences such as KDD, WWW, CIKM, PAKDD, and ACCV, as well as in SCIE journals including Applied Soft Computing and Elsevier Computers & Security. □ After receiving his PhD, he worked as a Principal Research Engineer at Hyundai Mobis, where he gained practical experience in automotive controller systems and vehicle cybersecurity. □ Sangyeop Lee, PhD stated, "Based on my research and industry experience so far, I will continue to advance research in deepfake detection and AI security, and do my best to help students develop practical capabilities." He also added, "I would like to express my sincere gratitude to Sungkyunkwan University and my advisor, Professor Simon Sungil Woo, for their guidance and support in helping me grow as a researcher." -
- 작성일 2026-03-16
- 조회수 910
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- [Mar 16, 2026] Professors Janghyun Kim and Jihyung Lee awarded 2025 SKKU-Fellowship
- Professors Janghyun Kim and Jihyung Lee of the College of Computing and Informatics awarded 2025 SKKU-Fellowship 11 Professors Selected for 2025 SKKU-Fellowship The University announced the selection of 11 professors as 2025 SKKU-Fellowship recipients: Professor Byungdeok Lee of the College of Liberal Arts, Professor Hwansu Yoon of the College of Natural Sciences, Professor Jinyong Lee of the College of Natural Sciences, Professor Janghyun Kim of the College of Computing and Informatics, Professor Jihyung Lee of the College of Computing and Informatics, Professor Junyeop Lee of the College of Engineering, Professor Hyukryul Choi of the College of Engineering, Professor Jechan Lee of the College of Engineering, Professor Wonseok Kim of the School of Medicine, Professor Sehoon Park of the School of Medicine, and Professor Juhee Cho of the Samsung Advanced Institute for Health Sciences & Technology. Starting this year, the University has significantly increased special incentive funding to provide extensive support to researchers representing the institution across diverse areas, including not only academic publications but also conferences and industry–academic cooperation. The 2025 SKKU-Fellowship recipients were selected through a rigorous evaluation process conducted by the selection committee, comprehensively considering the academic and qualitative excellence of research achievements as well as their global research impact. ▲ Professor Byungdeok Lee delivering his acceptance remarks. On behalf of the SKKU-Fellowship awardees, Professor Byungdeok Lee of the Department of Philosophy in the College of Liberal Arts delivered the acceptance remarks. He stated that he would contribute to creating future values for humanity by conducting research that establishes human subjectivity and ethical values that cannot be replaced by data and algorithms in the rapidly changing technological civilization, drawing great applause from the audience. ▲ Professor Hyukryul Choi, College of Engineering ▲ Professor Jihyung Lee, College of Computing and Informatics ▲ Professor Hwansu Yoon, College of Natural Sciences ▲ Professor Jinyong Lee, College of Natural Sciences ▲ Professor Janghyun Kim, College of Computing and Informatics ▲ Professor Junyeop Lee, College of Engineering ▲ Professor Byungdeok Lee, College of Liberal Arts President Jibeom Yoo emphasized, “The future of Sungkyunkwan University is built upon the challenging research and dedication of Fellowship awardees who open new academic horizons beyond global standards,” adding that the University will further strengthen groundbreaking research support and an honor system that enhances pride so that its most outstanding faculty members can achieve world-class accomplishments.
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- 작성일 2026-03-16
- 조회수 904
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- [Mar 05, 2026] Software Security Laboratory (Advisor: Sungjae Hwang) Paper Accepted to FSE '26
- A paper by Hong Jongchan (M.S. student) and Kim Jaewon (M.S. student) from the Software Security Laboratory (Advisor: Sungjae Hwang, https://softsec.skku.edu/) has been accepted for publication at Foundations of Software Engineering 2026 (FSE '26), a top-tier conference in the field of software engineering. The paper is scheduled to be presented in Canada in July 2026. As the adoption of electric vehicles rapidly expands, charging infrastructure is also growing quickly, increasing the importance of the Charging Station Management System (CSMS), which performs security-critical functions such as user authentication and billing. The CSMS communicates with charging stations (CS) through the Open Charge Point Protocol (OCPP). However, security testing for OCPP-based systems is highly challenging due to several factors: the complex message structure containing up to 48 fields, dependencies between fields within a message as well as across different messages, and state-based characteristics that require considering the states of both the CS and the CSMS. As a result, despite real-world attacks such as denial-of-service (DoS), unauthorized free charging, and data leakage, research on CSMS security and automated testing tools remains limited. To address these challenges, this paper proposes OCPPuzz, a specification-based fuzzing framework that automatically extracts message structures, field constraints, dependency rules, and CS–CSMS state transitions from the OCPP specification. The framework combines heuristic rule-based extraction techniques with large language models (LLMs). Evaluation on four open-source CSMS implementations revealed numerous severe specification violations and security vulnerabilities that could lead to DoS and unauthorized free charging. In total, 492 out of 930 implementation bugs were acknowledged, and among 134 specification bugs in the OCPP standard, 79 were fixed and 85 were acknowledged for further investigation. Abstract: Electric vehicles (EVs) are being rapidly adopted, with over 61,000 publicly accessible charging stations deployed across the United States as of 2024. A core component of this infrastructure is the Charging Station Management System (CSMS), which is responsible for security-critical tasks such as user authentication and billing. Given its importance, the CSMS has become a target of real-world attacks that have resulted in financial losses, data breaches, and denial-of-service(DoS) incidents. Nevertheless, research on CSMS security remains limited, and automated testing tools are lacking. Testing CSMS is challenging because they communicate with charging stations (CS) using the Open Charge Point Protocol (OCPP). Effective testing must contend with OCPP's complexity: 1) messages containing up to 48 fields, 2) inter- and intra-message field dependencies, and 3) its stateful nature, which requires tracking the states of both CS and CSMS during testing. To address these challenges, we present OCPPuzz, a specification-based fuzzing framework for CSMS. OCPPuzz automatically extracts message structures, field constraints, and dependency rules from the OCPP specification, as well as valid CS-CSMS state transitions described in its use case diagrams. To handle specifications expressed in natural language and semi-formal diagrams, OCPPuzz combines heuristic rule-based extraction with large language models (LLMs). We evaluated OCPPuzz on four open-source CSMS implementations and uncovered numerous deviations from the OCPP specification that led to critical security issues, including DoS and free charging. We reported 930 implementation bugs to the corresponding vendors, of which 492 have been acknowledged so far. In addition, we reported 134 specification bugs in OCPP to the Open Charge Alliance (OCA); 79 have been committed for fixes and 85 acknowledged for further investigation. We expect additional acknowledgments and fixes in the near future.
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- 작성일 2026-03-05
- 조회수 882
