Leonid Ryzhyk

PL
3papers
335citations
Novelty42%
AI Score23

3 Papers

PLFeb 25, 2018
Secure Serverless Computing Using Dynamic Information Flow Control

Kalev Alpernas, Cormac Flanagan, Sadjad Fouladi et al.

The rise of serverless computing provides an opportunity to rethink cloud security. We present an approach for securing serverless systems using a novel form of dynamic information flow control (IFC). We show that in serverless applications, the termination channel found in most existing IFC systems can be arbitrarily amplified via multiple concurrent requests, necessitating a stronger termination-sensitive non-interference guarantee, which we achieve using a combination of static labeling of serverless processes and dynamic faceted labeling of persistent data. We describe our implementation of this approach on top of JavaScript for AWS Lambda and OpenWhisk serverless platforms, and present three realistic case studies showing that it can enforce important IFC security properties with low overhead.

MLSep 19, 2017
Verifying Properties of Binarized Deep Neural Networks

Nina Narodytska, Shiva Prasad Kasiviswanathan, Leonid Ryzhyk et al.

Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural Networks, using the well-developed means of Boolean satisfiability. Our main contribution is a construction that creates a representation of a binarized neural network as a Boolean formula. Our encoding is the first exact Boolean representation of a deep neural network. Using this encoding, we leverage the power of modern SAT solvers along with a proposed counterexample-guided search procedure to verify various properties of these networks. A particular focus will be on the critical property of robustness to adversarial perturbations. For this property, our experimental results demonstrate that our approach scales to medium-size deep neural networks used in image classification tasks. To the best of our knowledge, this is the first work on verifying properties of deep neural networks using an exact Boolean encoding of the network.

SENov 23, 2016
Developing a Practical Reactive Synthesis Tool: Experience and Lessons Learned

Leonid Ryzhyk, Adam Walker

We summarise our experience developing and using Termite, the first reactive synthesis tool intended for use by software development practitioners. We identify the main barriers to making reactive synthesis accessible to software developers and describe the key features of Termite designed to overcome these barriers, including an imperative C-like specification language, an interactive source-level debugger, and a user-guided code generator. Based on our experience applying Termite to synthesising real-world reactive software, we identify several caveats of the practical use of the reactive synthesis technology. We hope that these findings will help define the agenda for future research on practical reactive synthesis.