Publications

Featured Publications

Fool Me If You Can: On the Robustness of Binary Code Similarity Detection Models against Semantics-preserving Transformations

Jiyong Uhm, Minseok Kim, Michalis Polychronakis and Hyungjoon Koo

In ACM International Conference on Foundations of Software Engineering, 2026 (FSE '26)

Binary analysis plays an essential role in cybersecurity, facilitating reverse engineering to reveal the inner workings of programs in the absence of source code. Traditional approaches, such as static and dynamic analysis, extract valuable insights from stripped binaries, but often demand substantial expertise and manual effort. Recent advances in deep learning have opened promising opportunities to enhance binary analysis by capturing latent features and disclosing underlying code semantics. Despite the growing number of binary analysis models based on machine learning, their robustness to adversarial code transformations remains underexplored. In this work, we evaluate the robustness of deep learning models for the task of binary code similarity detection (BCSD) under semantics-preserving transformations. The unique nature of machine instructions presents distinct challenges compared to the typical input perturbations found in other domains. To achieve our goal, we introduce asmFooler, a system that evaluates the resilience of BCSD models using a diverse set of adversarial code transformations that preserve functional semantics. We construct a dataset of 9,565 binary variants from 620 baseline samples by applying eight semantics-preserving transformations across six representative BCSD models. Our major findings highlight several key insights: i) model robustness highly relies on the design of the processing pipeline, including code pre-processing, model architecture, and internal feature selection, which collectively determine how code semantics are captured; ii) the effectiveness of adversarial transformations is bounded by a transformation budget, shaped by model-specific constraints such as input size limits and the expressive capacity of semantically equivalent instructions; iii) well-crafted adversarial transformations can be highly effective, even when introducing minimal perturbations; and iv) such transformations disrupt the model’s ability to recognize and focus on semantically significant instructions.

Paper arXiv Code Slides

UnPII: Unlearning Personally Identifiable Information with Quantifiable Exposure Risk

Intae Jeon, Yujeong Kwon, and Hyungjoon Koo

In the Software Engineering in Practice track of the International Conference on Software Engineering, 2026 (ICSE-SEIP '26)

The ever-increasing adoption of Large Language Models in critical sectors like finance, healthcare, and government raises privacy concerns regarding the handling of sensitive Personally Identifiable Information (PII) during training. In response, regulations such as European Union’s General Data Protection Regulation (GDPR) mandate the deletion of PII upon requests, underscoring the need for reliable and cost-effective data removal solutions. Machine unlearning has emerged as a promising direction for selectively forgetting data points. However, existing unlearning techniques typically apply a uniform forgetting strategy that neither accounts for the varying privacy risks posed by different PII attributes nor reflects associated business risks. In this work, we propose UnPII, the first PII-centric unlearning approach that prioritizes forgetting based on the risk of individual or combined PII attributes. To this end, we introduce the PII risk index (PRI), a composite metric that incorporates multiple dimension of risk factors: identifiability, sensitivity, usability, linkability, permanency, exposability, and compliancy. The PRI enables a nuanced evaluation of privacy risks associated with PII exposures and can be tailored to align with organizational privacy policies. To support realistic assessment, we systematically construct a synthetic PII dataset (e.g., 1,700 PII instances) that simulates realistic exposure scenarios. UnPII seamlessly integrates with established unlearning algorithms, such as Gradient Ascent, Negative Preference Optimization, and Direct Preference Optimization, without modifying their underlying principles. Our experimental results demonstrate that UnPII achieves the improvements of ac- curacy up to 11.8%, utility up to 6.3%, and generalizability up to 12.4%, respectively, while incurring a modest fine-tuning overhead of 27.5% on average during unlearning.

Paper arXiv Code Slides

A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis

Omar Abusabha, Jiyong Uhm, Tamer Abuhmed, and Hyungjoon Koo

In Network and Distributed System Security Symposium, 2026 (NDSS ’26)

A function inlining optimization is a widely used transformation in modern compilers, which replaces a call site with the callee’s body in need. While this transformation improves performance, it significantly impacts static features such as machine instructions and control flow graphs, which are crucial to binary analysis. Yet, despite its broad impact, the security impact of function inlining remains underexplored to date. In this paper, we present the first comprehensive study of function inlining through the lens of machine learning-based binary analysis. To this end, we dissect the inlining decision pipeline within the LLVM’s cost model and explore the combinations of the compiler options that aggressively promote the function inlining ratio beyond standard optimization levels, which we term extreme inlining. We focus on five ML-assisted security tasks (e.g., binary reversing), using 18 unique models to systematically evaluate their robustness under extreme inlining scenarios. Our extensive experiments reveal several significant findings: i) function inlining, though a benign transformation in intent, can (in)directly affect ML model behaviors, being potentially exploited by evading discriminative or generative ML models; ii) ML models relying on static features can be highly sensitive to inlining; iii) subtle compiler settings can be leveraged to deliberately craft evasive binary variants; and iv) inlining ratios vary substantially across applications and build configurations, undermining assumptions of consistency in training and evaluation of ML models.

Paper arXiv Code Slides

Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems

Minseok Kim, Hankook Lee, and Hyungjoon Koo

In the 41th Annual Computer Security Applications Conference, 2025 (ACSAC '25)

Large language models (LLMs) are reshaping numerous facets of our daily lives, leading widespread adoption as web-based services. Despite their versatility, LLMs face notable challenges, such as generating hallucinated content and lacking access to up-to-date information. Lately, to address such limitations, Retrieval-Augmented Generation (RAG) has emerged as a promising direction by generating responses grounded in external knowledge sources. A typical RAG system consists of i) a retriever that probes a group of relevant passages from a knowledge base and ii) a generator that formulates a response based on the retrieved content. However, as with other AI systems, recent studies demonstrate the vulnerability of RAG, such as knowledge corruption attacks by injecting misleading information. In response, several defense strategies have been proposed, including having LLMs inspect the retrieved passages individually or fine-tuning robust retrievers. While effective, such approaches often come with substantial computational costs. In this work, we introduce RAGDefender, a resource-efficient defense mechanism against knowledge corruption (i.e., by data poisoning) attacks in practical RAG deployments. RAGDefender operates during the post-retrieval phase, leveraging lightweight machine learning techniques to detect and filter out adversarial content without requiring additional model training or inference. Our empirical evaluations show that RAGDefender consistently outperforms existing state-of-the-art defenses across multiple models and adversarial scenarios: e.g., RAGDefender reduces the attack success rate (ASR) against the Gemini model from 0.89 to as low as 0.02, compared to 0.69 for RobustRAG and 0.24 for Discern-and-Answer when adversarial passages outnumber legitimate ones by a factor of four (4x).

Paper arXiv Code Slides

A Decade-long Landscape of Advanced Persistent Threats: Longitudinal Analysis and Global Trends

Shakhzod Yuldoshkhujaev, Mijin Jeon, Doowon Kim, Nick Nikiforakis, and Hyungjoon Koo

In the 32nd ACM Conference on Computer and Communications Security, 2025 (CCS '25)Distinguished Paper Award

An advanced persistent threat (APT) refers to a covert and long-term cyberattack, typically conducted by state-sponsored actors, targeting critical sectors and often remaining undetected for long periods. In response, collective intelligence from around the globe collaborates to identify and trace surreptitious activities, generating substantial documentation on APT campaigns publicly available on the web. While a multitude of prior works predominantly focus on specific aspects of APT cases, such as detection, evaluation, cyber threat intelligence, and dataset creation, limited attention has been devoted to revisiting and investigating these scattered dossiers in a longitudinal manner. The objective of our study lies in filling the gap by offering a macro perspective, connecting key insights and global trends in the past APT attacks. We systematically analyze six reliable sources - three focused on technical reports and another three on threat actors - examining 1,509 APT dossiers (i.e., totaling 24,215 pages) spanning from 2014 to 2023 (a decade), and identifying 603 unique APT groups in the world. To efficiently unearth relevant information, we employ a hybrid methodology that combines rule-based information retrieval with large-language-model-based search techniques. Our longitudinal analysis reveals shifts in threat actor activities, global attack vectors, changes in targeted sectors, and the relationships between cyberattacks and significant events, such as elections or wars, which provides insights into historical patterns in APT evolution. Over the past decade, 154 countries have been affected, primarily using malicious documents and spear phishing as the dominant initial infiltration vectors, and a noticeable decline in zero-day exploitation since 2016. Furthermore, we present our findings through interactive visualization tools, such as an APT map or a flow diagram, to facilitate intuitive understanding of the global patterns and trends in APT activities.

Paper arXiv Code Slides Web

R2I: A Relative Readability Metric for Decompiled Code

Haeun Eom, Dohee Kim, Sori Lim, Hyungjoon Koo, and Sungjae Hwang

In the ACM International Conference on the Foundations of Software Engineering, 2024 (FSE '24)

Decompilation is a process of converting a low-level machine code snippet back into a high-level programming language such as C. It serves as a basis to aid reverse engineers in comprehending the contextual semantics of the code. In this respect, commercial decompilers like Hex-Rays have made significant strides in improving the readability of decompiled code over time. While previous work has proposed the metrics for assessing the readability of source code, including identifiers, variable names, function names, and comments, those metrics are unsuitable for measuring the readability of decompiled code primarily due to i) the lack of rich semantic information in the source and ii) the presence of erroneous syntax or inappropriate expressions. In response, to the best of our knowledge, this work first introduces R2I, the Relative Readability Index, a specialized metric tailored to evaluate decompiled code in a relative context quantitatively. In essence, R2I can be computed by i) taking code snippets across different decompilers as input and ii) extracting pre-defined features from an abstract syntax tree. For the robustness of R2I, we thoroughly investigate the enhancement efforts made by existing decompilers and academic research to promote code readability, identifying 31 features to yield a reliable index collectively. Besides, we conducted a user survey to capture subjective factors such as one’s coding styles and preferences. Our empirical experiments demonstrate that R2I is a versatile metric capable of representing the relative quality of decompiled code (e.g., obfuscation, decompiler updates) and being well aligned with human perception in our survey.

Paper Code

BENZENE: A Practical Root Cause Analysis System with an Under-Constrained State Mutation

Younggi Park, Hwiwon Lee, Jinho Jung, Hyungjoon Koo, and Huy Kang Kim

In the 45th IEEE Symposium on Security and Privacy, 2024 (S&P ’24)Distinguished Paper Award

Fuzzing has demonstrated great success in bug discovery and plays a crucial role in software testing today. Despite the increasing popularity of fuzzing, automated root cause analysis (RCA) has drawn less attention. One of the recent advances in RCA is crash-based statistical debugging, which leverages the behavioral differences in program execution between crash-triggered and non-crashing inputs. Hence, obtaining non-crashing behaviors close to the original crash is crucial but challenging with previous approaches (e.g., fuzzing). In this paper, we present BENZENE, a practical end-to-end RCA system that facilitates a fully automated crash diagnosis. To this end, we introduce a novel technique, called under-constrained state mutation, that generates both crashing and non-crashing behaviors for effective and efficient RCA. We design and implement the BENZENE prototype, and evaluate it with 60 vulnerabilities in the wild. Our empirical results demonstrate that BENZENE not only surpasses in performance (i.e., root cause ranking), but also achieves superior results in both speed (4.6 times faster) and memory footprint (31.4 times less) on average than prior approaches.

Paper Code

A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing

Hyunjin Kim, Jinyeong Bak, Kyunghyun Cho, and Hyungjoon Koo

In the 18th ACM Asia Conference on Computer and Communications Security, 2023 (ASIACCS ’23)

Reverse engineering of a stripped binary has a wide range of applications, yet it is challenging mainly due to the lack of contextually useful information within. Once debugging symbols (e.g., variablenames, types, function names) are discarded, recovering such informationis not technically viable with traditional approacheslike static or dynamic binary analysis. We focus on a functionsymbol name recovery, which allows a reverse engineer to gaina quick overview of an unseen binary. The key insight is that awell-developed program labels a meaningful function name thatdescribes its underlying semantics well. In this paper, we presentAsmDepictor, the Transformer-based framework that generates afunction symbol name from a set of assembly codes (i.e., machine instructions),which consists of three major components: binary coderefinement, model training, and inference. To this end, we conductsystematic experiments on the effectiveness of code refinement thatcan enhance an overall performance. We introduce the per-layerpositional embedding and Unique-softmax for AsmDepictor sothat both can aid to capture a better relationship between tokens.Lastly, we devise a novel evaluation metric tailored for a short descriptionlength, the Jaccard* score. Our empirical evaluation showsthat the performance of AsmDepictor by far surpasses that of thestate-of-the-art models up to around 400%. The best AsmDepictormodel achieves an F1 of 71.5 and Jaccard* of 75.4.

Paper

International Conferences and Journals

Localizing Vulnerabilities under Function Inlining Toward Precise Binary Patching (To appear)

Mijin Jeon, Yiyue Zhang and Hyungjoon Koo

In the 42th Annual Computer Security Applications Conference, 2026 (ACSAC '26)

Fool Me If You Can: On the Robustness of Binary Code Similarity Detection Models against Semantics-preserving Transformations

Paper arXiv Code Slides

Jiyong Uhm, Minseok Kim, Michalis Polychronakis and Hyungjoon Koo

In ACM International Conference on Foundations of Software Engineering, 2026 (FSE '26)

UnPII: Unlearning Personally Identifiable Information with Quantifiable Exposure Risk

Paper arXiv Code Slides

Intae Jeon, Yujeong Kwon, and Hyungjoon Koo

In the Software Engineering in Practice track of the International Conference on Software Engineering, 2026 (ICSE-SEIP '26)

A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis

Paper arXiv Code Slides

Omar Abusabha, Jiyong Uhm, Tamer Abuhmed, and Hyungjoon Koo

In Network and Distributed System Security Symposium, 2026 (NDSS ’26)

Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems

Paper arXiv Code Slides

Minseok Kim, Hankook Lee, and Hyungjoon Koo

In the 41th Annual Computer Security Applications Conference, 2025 (ACSAC '25)

A Decade-long Landscape of Advanced Persistent Threats: Longitudinal Analysis and Global Trends

Paper arXiv Code Slides Web

Shakhzod Yuldoshkhujaev, Mijin Jeon, Doowon Kim, Nick Nikiforakis, and Hyungjoon Koo

In the 32nd ACM Conference on Computer and Communications Security, 2025 (CCS '25)Distinguished Paper Award

BOOTKITTY: A Stealthy Bootkit-Rootkit Against Modern Operating Systems

Paper

Junho Lee, Jihoon Kwon, HyunA Seo, Myeongyeol Lee, Hyungyu Seo, Jinho Jung, and Hyungjoon Koo

In the 19th USENIX WOOT Conference on Offensive Technologies, 2025 (WOOT '25)

Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models

Paper

Fujiao Ji, Kiho Lee, Hyungjoon Koo, Wenhao You, Euijin Choo, Hyoungshick Kim, and Doowon Kim

In the 34nd USENIX Conference on Security Symposium, 2025 (USENIX '25)

An Empirical Study of Black-box based Membership Inference Attacks on a Real-World Dataset

Paper

Yujeong Kwon, Simon S. Woo, and Hyungjoon Koo

International Symposium on Foundations and Practice of Security, 2024 (FPS '24)

R2I: A Relative Readability Metric for Decompiled Code

Paper Code

Haeun Eom, Dohee Kim, Sori Lim, Hyungjoon Koo, and Sungjae Hwang

In the ACM International Conference on the Foundations of Software Engineering, 2024 (FSE '24)

BinAdapter: Leveraging Continual Learning for Inferring Function Symbol Names in a Binary

Paper Code

Nozima Murodova and Hyungjoon Koo

In the 19th ACM Asia Conference on Computer and Communications Security, 2024 (ASIACCS ’24)

ToolPhet: Inference of Compiler Provenance from Stripped Binaries with Emerging Compilation Toolchains

Paper

Hohyeon Jang, Nozima Murodova, and Hyungjoon Koo

IEEE Access (2024)

Demystifying the Regional Phishing Landscape in South Korea

Hyunjun Park, Kyungchan Lim, Doowon Kim, Donghyun Yu, and Hyungjoon Koo

IEEE Access (2023)

BENZENE: A Practical Root Cause Analysis System with an Under-Constrained State Mutation

Paper Code

Younggi Park, Hwiwon Lee, Jinho Jung, Hyungjoon Koo, and Huy Kang Kim

In the 45th IEEE Symposium on Security and Privacy, 2024 (S&P ’24)Distinguished Paper Award

Binary Code Representation with Well-balanced Instruction Normalization

Paper arXiv

Hyungjoon Koo, Soyeon Park, Daejin Choi, and Taesoo Kim

IEEE Access (2023)

SmartMark: Software Watermarking Scheme for Smart Contracts

Paper

Taeyoung Kim, Yunhee Jang, Chanjong Lee, Hyungjoon Koo, and Hyoungshick Kim

In the 45th IEEE/ACM International Conference on Software Engineering, 2023 (ICSE ’23)

A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing

Paper

Hyunjin Kim, Jinyeong Bak, Kyunghyun Cho, and Hyungjoon Koo

In the 18th ACM Asia Conference on Computer and Communications Security, 2023 (ASIACCS ’23)

Practical Binary Code Similarity Detection with BERT-based Transferable Similarity Learning

Paper Code Slides

Sunwoo Ahn, Seonggwan Ahn, Hyungjoon Koo, and Yunheung Paek

In the 38th Annual Computer Security Applications Conference (ACSAC ’22)

DeView: Confining Progressive Web Applications by Debloating Web APIs

Paper Code

ChangSeok Oh, Sangho Lee, Chenxiong Qian, Hyungjoon Koo, and Wenke Lee

In the 38th Annual Computer Security Applications Conference (ACSAC ’22)

IoTivity Packet Parser for Encrypted Messages in Internet of Things

Paper

Hyeonah Jung, Hyungjoon Koo, and Jaehoon (Paul) Jeong

In the 24th International Conference on Advanced Communications Technology (ICACT ’22)

Software Watermarking via a Binary Function Relocation

Paper

Honggoo Kang, Yonghwi Kwon, Sangjin Lee, and Hyungjoon Koo

In the 37th Annual Computer Security Applications Conference (ACSAC ’21)

A Look Back on a Function Identification Problem

Paper

Hyungjoon Koo, Soyeon Park, and Taesoo Kim

In the 37th Annual Computer Security Applications Conference (ACSAC ’21)

Slimium: Debloating the Chromium Browser with Feature Subsetting

Paper

Chenxiong Qian, Hyungjoon Koo, Changseok Oh, Taesoo Kim, and Wenke Lee

In the 27th ACM Conference on Computer and Communications Security (CCS ’20)

Compiler-assisted Code Randomization

Paper Code

Hyungjoon Koo, Yaohui Chen, Long Lu, Vasileios P. Kemerlis, and Michalis Polychronakis

In the 39th IEEE Symposium on Security & Privacy, 2018 (S&P ’18)

Defeating Zombie Gadgets by Re-randomizing Code Upon Disclosure

Paper

Micah Morton, Hyungjoon Koo, Forrest Li, Kevin Z. Snow, Michalis Polychronakis, and Fabian Monrose

In the 9th International Symposium on Engineering Secure Software and Systems, 2017 (ESSoS ’17)

Return to the Zombie Gadgets: Undermining Destructive Code Reads via Code-Inference Attacks

Paper

Kevin Z. Snow, Roman Rogowski, Jan Werner, Hyungjoon Koo, Fabian Monrose and Michalis Polychronakis

In the 37th IEEE Symposium on Security and Privacy, 2016 (S&P ’16)

Juggling the Gadgets: Binary-level Code Randomization using Instruction Displacement

Paper Code

Hyungjoon Koo and Michalis Polychronakis

In the 11th ACM Asia Conference on Computer and Communications Security, 2016 (ASIACCS ’16)

Identifying Traffic Differentiation in Mobile Networks

Paper

Arash Molavi Kakhki, Abbas Razaghpanah, Anke Li, Hyungjoon Koo, Rajeshkumar Golani, David Choffnes, Phillipa Gill, and Alan Mislove

In the 15th ACM Internet Measurement Conference, 2015 (IMC ’15)

Domestic Conferences and Journals

Recoverability Analysis of Personally Identifiable Information in Unlearned Language Models

Paper

Yujeong Kwon, and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’26)

Post-hoc Ranking of Poisoned Training Samples in LoRA-Fine-Tuned Models

Paper

Byungsoo Kang, Minseok Kim, and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’26)

A Comparative Analysis of Watermarking Techniques for Large Language Model-Generated Text

Paper

Gahyun Baek, and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’26)

A Study of Information Leakage Across Topologies and Languages in LLM-based Multi-Agent Systems

Paper

Gahyun Baek, Minseok Kim and Hyungjoon Koo

Korea Computer Congress 한국컴퓨터종합학술대회 (2026)

Analysis on Rust Compiler Logic Bug in Safe Rust

Paper

Jiyong Uhm, Hyoungjun Jeon and Hyungjoon Koo

Korea Computer Congress 한국컴퓨터종합학술대회 (2026)

Trends in Model Context Protocol (MCP) Tool Poisoning Attacks and Defenses

Paper

Woomin Kim, Yujeong Kwon and Hyungjoon Koo

Korea Computer Congress 한국컴퓨터종합학술대회 (2026)

Security of Autonomous AI Agents: Trust Boundary-Based Attack Surface Analysis and Trends

Paper

Minseok Kim and Hyungjoon Koo

Review of KIISC (2026)

Online Messenger Forensics Across Platforms: A Systematic Comparative Study

Paper

Suyeon Lee, Yuldoshkhujaev Shakhzod, and Hyungjoon Koo

Journal of The Korea Institute of information Security & Cryptology (2025)

LLM-Based Drug Term Detection in Korean Messenger Conversations

Paper Code

Minseok Kim and Hyungjoon Koo

Journal of The Korea Institute of information Security & Cryptology (2025)

A Risk Assessment Framework for Personally Identifiable Information Leakage in Generative AI Models through its Quantification

Paper

Intae Jeon and Hyungjoon Koo

Journal of The Korea Institute of information Security & Cryptology (2025)

Evolving Attack Tactics against Messaging Applications

Paper

Shakhzod Yuldoshkhujaev, Suyeon Lee, and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’25)

Analysis of Watermarking for AI-generated Text

Paper

Giuk Kwon, Minseok Kim, and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’25)

Trends in Securing Unsafe Regions for Rust

Paper

Hyungjun Jeon, Jiyong Uhm, and Hyungjoon Koo

Conference on Information Security and Cryptography-Summer 한국정보보호학회 하계학술대회 (CISC-S ’25)

Security Analysis of Agentic Communication Protocols: Model Context Protocol (MCP) and Agent-to-Agent (A2A)

Paper

Yiyue Zhang, Minseok Kim, and Hyungjoon Koo

Conference on Information Security and Cryptography-Summer 한국정보보호학회 하계학술대회 (CISC-S ’25)

Investigating the Acquisition and Analysis of Digital Artifacts for Messaging Applications

Paper

Suyeon Lee, Yuldoshkhujaev Shakhzod, and Hyungjoon Koo

Conference on Information Security and Cryptography-Summer 한국정보보호학회 하계학술대회 (CISC-S ’25)

Trends in LLM-assisted Attacks

Paper

Mijin Jeon, Yujeong Kwon, and Hyungjoon Koo

Conference on Information Security and Cryptography-Summer 한국정보보호학회 하계학술대회 (CISC-S ’25)

Advanced Persistent Threats: Addressed and Open Research Questions

Paper

Shakhzod Yuldoshkhujaev and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’24)

Trends in Attacks and Defenses against Retrieval-Augmented Generation (RAG) Systems

Paper

Minseok Kim and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’24)

Trends in Deep-learning-based Models for Anomaly Detection in Log Records

Paper

Suyeon Lee and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’24)

RoBERTa-based Obfuscated Binary Code Similarity Detection

Paper

Yujeong Kwon, Jiyong Uhm, and Hyungjoon Koo

Conference on Information Security and Cryptography-Summer 한국정보보호학회 하계학술대회 (CISC-S ’24)Best Paper Award

Dynamic Analysis of Obfuscated Code with a 64-bit SGN Packer

Paper

Jiyong Uhm, Hyongjun Jeon, and Hyungjoon Koo

Conference on Information Security and Cryptography-Summer 한국정보보호학회 하계학술대회 (CISC-S ’24)

ChatPub: Retrieval Augmented Generation-based Service to Aid in Finding Relevant Policies for Korean Youth

Paper

Gangsan Kim, Jinho Park, Seongbin Yang, Changmin Jeon, and Hyungjoon Koo

Annual Spring Conference of KIPS (ASK’24)

Baseball Simulation Game Service Based on Baseball Metrics

Paper

Chaewon Ko, Changwoo Shim, Hyunchang Shin, and Hyungjoon Koo

Annual Spring Conference of KIPS (ASK’24)

Trends in Machine Unlearning via Continual Learning to Mitigate Privacy Risks

Paper

Yujeong Kwon and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’23)

Trends in Membership Inference Attacks and Defenses

Paper

Nozima Murodova and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’23)

Trends in Explainable Artificial Intelligence for Security

Paper

Jiyong Uhm, Hyungjoon Jeon, and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’23)

An Unreal Engine Plugin for Text-based Runtime Animation Generation with a Motion Diffusion Model

Paper

Suho Park, Jaehoon Lee, YongHyeon Jo, Haechan Je, Daniel Cha, and Hyungjoon Koo

Annual Fall Conference of KIPS (ASK ’23)

Kingomanager: A Personalized Information-providing Application with a Recommendation System for University Students

Paper

Shingyu Kang, JunWoo Kim, ChoongHyeon Park, and Hyungjoon Koo

Annual Spring Conference of KIPS (ASK’23)

Vulnerability Analysis of CoAP Using a Coverage-based CoAP Fuzzer

Paper

Sechang Lim and Hyungjoon Koo

Annual Spring Conference of KIPS (ASK’23)

A Recommendation System by Extracting Scholarship Information with a BERT’s Q&A Model

Paper

Hohyeon Jang and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’22)Best Paper Award

A Study of Rarely Appeared Instructions in an Executable Binary

Paper

Nozima Murodova and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’22)

Server-client Based Reversing Tools with a Prediction Model for Function Names

Paper

Seok Kim, Hyungjoon Jeon, Hyunjin Kim, and Hyungjoon Koo

Conference on Information Security and Cryptography-Winter 한국정보보호학회 동계학술대회 (CISC-W ’22)

Phishing URL Detection with Text-CNN

Paper

Sechang Lim and Hyungjoon Koo

Conference on Information Security and Cryptography-Summer 한국정보보호학회 하계학술대회 (CISC-S ’22)

Workshops and Posters

On the Learnability, Robustness, and Adaptability of Deep Learning Models for Obfuscation-applied Code

Paper Code

Jiyong Uhm, Yujeong Kwon, and Hyungjoon Koo

Workshop on Software Understanding and Reverse Engineering (SURE'25)

Evaluating Password Composition Policy and Password Meters of Popular Websites

Paper

Kyungchan Lim, Joshua Hankyul Kang, Matthew Dixson, Hyungjoon Koo, and Doowon Kim

Workshop on Designing Security for the Web (SecWeb ’23)

Inference of Compiler Provenance from Malware

Paper

Hohyun Jang and Hyungjoon Koo

Poster in the 22nd World Conference on Information Security Applications (WISA ’21)

Configuration-Driven Software Debloating

Paper

Hyungjoon Koo, Seyedhamed Ghavamnia, and Michalis Polychronakis

In the 12th European Workshop on Systems Security, 2019 (EuroSec’19)

The Politics of Routing: Investigating the Relationship between AS Connectivity and Internet Freedom

Paper

Rachee Singh, Hyungjoon Koo, Najmehalsadat Miramirkhani, Fahimeh Mirhaj, Leman Akoglu, and Phillipa Gill

In the 6th USENIX Workshop on Free and Open Communications on the Internet, 2016 (FOCI ’16)