P
Petar Tsankov
Researcher at ETH Zurich
Publications - 43
Citations - 2670
Petar Tsankov is an academic researcher from ETH Zurich. The author has contributed to research in topics: Fuzz testing & Access control. The author has an hindex of 18, co-authored 42 publications receiving 1688 citations. Previous affiliations of Petar Tsankov include Georgia Institute of Technology & IBM.
Papers
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Proceedings ArticleDOI
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
TL;DR: This work presents AI2, the first sound and scalable analyzer for deep neural networks, and introduces abstract transformers that capture the behavior of fully connected and convolutional neural network layers with rectified linear unit activations (ReLU), as well as max pooling layers.
Proceedings ArticleDOI
Securify: Practical Security Analysis of Smart Contracts
Petar Tsankov,Andrei Marian Dan,Dana Drachsler-Cohen,Arthur Gervais,Florian Bünzli,Martin Vechev +5 more
TL;DR: Securify as mentioned in this paper is a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property.
Proceedings ArticleDOI
VerX: Safety Verification of Smart Contracts
TL;DR: VerX is the first automated verifier able to prove functional properties of Ethereum smart contracts, based on a careful combination of three techniques, enabling it to automatically verify temporal properties of infinite- state smart contracts.
Proceedings ArticleDOI
Learning to Fuzz from Symbolic Execution with Application to Smart Contracts
TL;DR: ILF (for Imitation Learning based Fuzzer) is effective, it is fast, generating 148 transactions per second, it outperforms existing fuzzers, and it detects more vulnerabilities than existing fuzzing and symbolic execution tools for Ethereum.
Proceedings ArticleDOI
Statistical Deobfuscation of Android Applications
TL;DR: This work phrases the layout deobfuscation problem of Android APKs as structured prediction in a probabilistic graphical model, instantiates this model with a rich set of features and constraints that capture the Android setting, ensuring both semantic equivalence and high prediction accuracy, and shows how to leverage powerful inference and learning algorithms to achieve overall precision and scalability of the probabilism predictions.