정다래 교수
Dae R. Jeong
서울대학교 · 컴퓨터과학
연구실 소개
정다래 교수의 연구실은 시스템 소프트웨어의 안정성과 보안을 강화하기 위한 핵심 기술을 연구하고 있습니다. 특히 커널 수준의 동시성 버그를 효율적으로 탐지하기 위한 퍼지 테스팅 기반 정적 분석 기법과 하이브리드 퍼지 테스팅 기법을 개발하며, 다기기 환경에서의 애플리케이션 협업과 기기 간 기능 공유를 가능하게 하는 플랫폼 기술도 함께 연구하고 있습니다. 이는 모바일 및 스마트 디바이스의 사용자 경험을 향상시키는 데 기여합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15A data race in a kernel is an important class of bugs, critically impacting the reliability and security of the associated system. As a result of a race, the kernel may become unresponsive. Even worse, an attacker may launch a privilege escalation attack to acquire root privileges. In this paper, we propose Razzer, a tool to find race bugs in kernels. The core of Razzer is in guiding fuzz testing towards potential data race spots in the kernel. Razzer employs two techniques to find races efficie
Hybrid fuzzing, combining symbolic execution and fuzzing, is a promising approach for vulnerability discovery because each approach can complement the other. However, we observe that applying hybrid fuzzing to kernel testing is challenging because the following unique characteristics of the kernel make a naive adoption of hybrid fuzzing inefficient: 1) having indirect control transfers determined by system call arguments, 2) controlling and matching internal system state via system calls, and 3)
In recent years, the explosion of diverse smart devices such as mobile phones, TVs, watches, and even cars, has completely changed our lives. We communicate with friends through social network services (SNSs) whenever we want, buy stuff without visiting shops, and enjoy multimedia wherever we are, thanks to these devices. However, these smart devices cannot simply interact with each other even though they are right next to each other. For example, when you want to read a PDF stored on a smartpho
The growing trend of multi-device ownerships creates a need and an opportunity to use applications across multiple devices. However, in general, the current app development and usage still remain within the single-device paradigm, falling far short of user expectations. For example, it is currently not possible for a user to dynamically partition an existing live streaming app with chatting capabilities across different devices, such that she watches her favorite broadcast on her smart TV while
Discovering kernel concurrency bugs through fuzzing is challenging. Identifying kernel concurrency bugs, as opposed to non-concurrency bugs, necessitates an analysis of possible interleavings between two or more threads. However, because the search space of thread interleaving is vast, it is impractical to investigate all conceivable thread interleavings. To explore the vast search space, most previous approaches perform random or simple heuristic searches without having coverage for thread inte
Despite the physical advance of an existing single-cell battery system, mobile users are still suffering from low battery anxiety. With a careful analysis of users' battery usage behavior collected for 19,855 hours, we propose a heterogeneous battery system, MixMax, consisting of three complementary battery types tailored to minimizing the low battery time. While composing a heterogeneous battery system opens up a chance to simultaneously improve the capacity and the charging speed, one must fac
A recent trend in the global mobile/IoT industry is the emergence of next-generation smart devices with various screens, thus mobile/IoT market leaders are highly focused on building a new multi-device computing ecosystem based on such new smart devices. Market leaders are not only simply varying their screen sizes, but also competitively launching new devices equipped with innovative screens like foldable and dual-screen phones. However, the current mobile computing ecosystem is restricted by t
Kernel concurrency bugs are notoriously difficult to identify, while their consequences severely threaten the reliability and security of the entire system. Especially in the kernel, developers should consider not only locks but also memory barriers to prevent out-of-order execution from breaking the correctness of concurrent execution. Incorrect use of memory barriers may cause non-intuitive concurrency bugs that manifest due to out-of-order execution, which we refer to as OoO bugs.
Low-battery anxiety has emerged as a result of growing dependence on mobile devices, where the anxiety arises when the battery level runs low. While battery life can be extended through power-efficient hardware and software optimization techniques, low-battery anxiety will still remain a phenomenon as long as mobile devices rely on batteries. In this paper, we investigate how an accurate real-time energy consumption prediction at the application-level can improve the user experience in low-batte
Kernel concurrency failures are notoriously difficult to identify and diagnose their fundamental reason, the root cause. Kernel concurrency bugs frequently involve challenging patterns such as multi-variable races, data races with asynchronous kernel threads, and pervasive benign races. We perform an in-depth study of real-world kernel concurrency bugs and elicit three requirements: comprehensiveness, pattern-agnostic, and conciseness.
The growing trend of multi-device ownerships creates opportunities to use applications across devices. However, the current methods of app development/usage remain in the single-device paradigm, which is far below user expectations. For example, it is currently impossible for users to dynamically partition an existing app across different devices to utilize multiple surfaces. We introduce FLUID, a novel multi-device platform that supports simultaneous operation of multiple devices. FLUID aims to
The growing trend of multi-device ownerships creates a need and an opportunity to use applications across multiple devices. However, in general, the current app development and usage still remain within the single-device paradigm, falling far short of user expectations. We present FLUID, a new multi-device platform that allows users to migrate or replicate individual user interfaces (UIs) of a single app on multiple devices. In addition, FLUID aims to require no extra development effort to suppo
Even with advances in single-cell batteries, mobile users still experience low battery anxiety. By analyzing 19,855 hours of user behavior, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MixMax</b>, a heterogeneous battery system consisting of three complementary battery types tailored to minimizing low battery time. While the heterogeneous battery system offers an opportunity to simultaneously improve capacity and charging speed, one m
This artifact contains the research artifacts for the paper "DMGuard: Safeguarding Kernels from Physical-Page Use-After-Free Vulnerabilities", to appear in USENIX Security 2026. DMGuard is a runtime mitigation designed to comprehensively address physical-page use-after-free vulnerabilities across diverse translation domains, including CPU, GPU, and IOMMU page tables. It utilizes a lightweight, lockless state machine to track physical page lifecycles and detect dangling mappings. Artifact Content
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