What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Microsoft Windows
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 most cited work include:
- Panorama: capturing system-wide information flow for malware detection and analysis (668 citations)
- BitBlaze: A New Approach to Computer Security via Binary Analysis (603 citations)
- DroidScope: seamlessly reconstructing the OS and Dalvik semantic views for dynamic Android malware analysis (570 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Computer security (36.27%)
- Malware (35.29%)
- Android (21.57%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (14.71%)
- Machine learning (10.78%)
- Computer security (36.27%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- DeepBinDiff: Learning Program-Wide Code Representations for Binary Diffing (7 citations)
- Automatic Generation of Adversarial Examples for Interpreting Malware Classifiers. (6 citations)
- Parallel Space Traveling: A Security Analysis of App-Level Virtualization in Android (2 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Microsoft Windows
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.
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