H
Heng Yin
Researcher at University of California, Riverside
Publications - 107
Citations - 8554
Heng Yin is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Malware & Android (operating system). The author has an hindex of 33, co-authored 100 publications receiving 7162 citations. Previous affiliations of Heng Yin include Carnegie Mellon University & University of California.
Papers
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Proceedings ArticleDOI
Panorama: capturing system-wide information flow for malware detection and analysis
TL;DR: This work proposes a system, Panorama, to detect and analyze malware by capturing malicious information access and processing behavior, which separates these malicious applications from benign software.
Book ChapterDOI
BitBlaze: A New Approach to Computer Security via Binary Analysis
Dawn Song,David Brumley,Heng Yin,Juan Caballero,Ivan Jager,Min Gyung Kang,Zhenkai Liang,James Newsome,Pongsin Poosankam,Prateek Saxena +9 more
TL;DR: An overview of the BitBlaze project, a new approach to computer security via binary analysis that focuses on building a unified binary analysis platform and using it to provide novel solutions to a broad spectrum of different security problems.
Proceedings Article
DroidScope: seamlessly reconstructing the OS and Dalvik semantic views for dynamic Android malware analysis
Lok Kwong Yan,Heng Yin +1 more
TL;DR: DroidScope is presented, an Android analysis platform that continues the tradition of virtualization-based malware analysis and reconstructs both the OS-level and Java-level semantics simultaneously and seamlessly.
Book ChapterDOI
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
TL;DR: In this article, a robust and lightweight classifier is proposed to mitigate Android malware installation through providing relevant features to malware behavior captured at API level, and evaluated different classifiers using the generated feature set.
Proceedings ArticleDOI
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs
TL;DR: A novel semantic-based approach that classifies Android malware via dependency graphs that is capable of detecting zero-day malware with a low false negative rate and an acceptable false positive rate while tolerating minor implementation differences is proposed.