Heng Yin spends much of his time researching Computer security, Malware, Malware analysis, Android and Artificial intelligence. His Password study, which is part of a larger body of work in Computer security, is frequently linked to Quality, bridging the gap between disciplines. His Malware study frequently intersects with other fields, such as Keystroke logging.
His Malware analysis course of study focuses on Cryptovirology and Taint checking. Heng Yin has included themes like HTML5 and Mobile device in his Android study. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Android malware.
His primary scientific interests are in Computer security, Malware, Android, Operating system and Malware analysis. The study incorporates disciplines such as Software, Static analysis and Source code in addition to Computer security. His research in Malware intersects with topics in Machine learning, Keystroke logging and Artificial intelligence.
His Android research includes elements of World Wide Web and Mobile device. As part of one scientific family, Heng Yin deals mainly with the area of Operating system, narrowing it down to issues related to the Spec#, and often Interface. His work carried out in the field of Malware analysis brings together such families of science as Taint checking, Instrumentation, Theoretical computer science and Hardware virtualization.
Artificial intelligence, Machine learning, Computer security, Software and Fuzz testing are his primary areas of study. His study in Machine learning is interdisciplinary in nature, drawing from both Software bug, Java, Malware, Control flow and Assembly language. His work on Android malware as part of general Malware research is often related to tf–idf, thus linking different fields of science.
His Computer security research incorporates elements of Adversarial system, Taint checking, Static analysis and Sequence learning. In his research on the topic of Software, Programming language, Binary number, Data mining, Memory corruption and Representation is strongly related with Code. His research investigates the connection between Fuzz testing and topics such as Vulnerability discovery that intersect with issues in Android.
His main research concerns Code, Software, Computer security, Programming language and Binary number. Heng Yin has researched Code in several fields, including Representation, Key, Data mining and Memory corruption. His research integrates issues of Hypervisor, Protocol, Data structure and Channel in his study of Software.
Heng Yin is interested in Malware, which is a branch of Computer security.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Panorama: capturing system-wide information flow for malware detection and analysis
Heng Yin;Dawn Song;Manuel Egele;Christopher Kruegel.
computer and communications security (2007)
DroidScope: seamlessly reconstructing the OS and Dalvik semantic views for dynamic Android malware analysis
Lok Kwong Yan;Heng Yin.
usenix security symposium (2012)
BitBlaze: A New Approach to Computer Security via Binary Analysis
Dawn Song;David Brumley;Heng Yin;Juan Caballero.
international conference on information systems security (2008)
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
Yousra Aafer;Wenliang Du;Heng Yin.
international conference on security and privacy in communication systems (2013)
Polyglot: automatic extraction of protocol message format using dynamic binary analysis
Juan Caballero;Heng Yin;Zhenkai Liang;Dawn Song.
computer and communications security (2007)
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs
Mu Zhang;Yue Duan;Heng Yin;Zhiruo Zhao.
computer and communications security (2014)
Renovo: a hidden code extractor for packed executables
Min Gyung Kang;Pongsin Poosankam;Heng Yin.
Proceedings of the 2007 ACM workshop on Recurring malcode (2007)
Dynamic spyware analysis
Manuel Egele;Christopher Kruegel;Engin Kirda;Heng Yin.
usenix annual technical conference (2007)
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
Xiaojun Xu;Chang Liu;Qian Feng;Heng Yin.
computer and communications security (2017)
Automatically Identifying Trigger-based Behavior in Malware
David Brumley;Cody Hartwig;Zhenkai Liang;James Newsome.
Botnet Detection (2008)
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