J
Johannes Kinder
Researcher at Bundeswehr University Munich
Publications - 45
Citations - 2218
Johannes Kinder is an academic researcher from Bundeswehr University Munich. The author has contributed to research in topics: Symbolic execution & Malware. The author has an hindex of 21, co-authored 44 publications receiving 1841 citations. Previous affiliations of Johannes Kinder include Microsoft & Technische Universität Darmstadt.
Papers
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Proceedings ArticleDOI
Efficient state merging in symbolic execution
TL;DR: A way to automatically choose when and how to merge states such that the performance of symbolic execution is significantly increased and query count estimation, a method for statically estimating the impact that each symbolic variable has on solver queries that follow a potential merge point, is presented.
Proceedings Article
{TESSERACT}: Eliminating Experimental Bias in Malware Classification across Space and Time
TL;DR: In this article, the authors argue that results are commonly inflated due to two pervasive sources of experimental bias: spatial bias caused by distributions of training and testing data that are not representative of a real-world deployment.
Proceedings ArticleDOI
DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware
Guillermo Suarez-Tangil,Santanu Kumar Dash,Mansour Ahmadi,Johannes Kinder,Giorgio Giacinto,Lorenzo Cavallaro +5 more
TL;DR: DroidSieve is proposed, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation, and exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware.
Book ChapterDOI
Detecting malicious code by model checking
TL;DR: This paper introduces the specification language CTPL (Computation Tree Predicate Logic) which extends the well-known logic CTL, and describes an efficient model checking algorithm which is able to detect a large number of worm variants with a single specification.
Efficient State Merging in Symbolic Execution.
TL;DR: In this article, the authors present query count estimation, a method for statically estimating the impact that each symbolic variable has on solver queries that follow a potential merge point; states are then merged only when doing so promises to be advantageous.