Habib Saissi, Stefan Winter, Oliver Schwan, Karthik Pattabiraman, and Neeraj Suri, IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2020. (Acceptance Rate: 16.5%). [PDF | Talk] (Code)
Abstract: Modern computing systems typically relax execution determinism, for instance by allowing the CPU scheduler to inter-leave the execution of several threads. While beneficial for performance, execution non-determinism affects programs’ execution traces and hampers the comparability of repeated executions. We present TraceSanitizer, a novel approach for execution trace comparison in Error Propagation Analyses (EPA) of multi-threaded programs. TraceSanitizer can identify and compensate for non-determinism caused either by dynamic memory allocation or by non-deterministic scheduling. We formulate a condition under which TraceSanitizer is guaranteed to achieve a 0% false positive rate and automate its verification using Satisfiability Modulo Theory (SMT) solving techniques. TraceSanitizer is comprehensively evaluated using execution traces from the PARSEC and Phoenix benchmarks. In contrast with other approaches, TraceSanitizer eliminates false positives without increasing the false negative rate (for a specific class of programs), with reasonable performance overheads.
-
Recent Papers
- OneOS: Distributed Operating System for the Edge-to-Cloud Continuum
- RAVAGE: Robotic Autonomous Vehicles’ Attack Generation Engine
- Reentrancy Redux: The Evolution of Real-World Reentrancy Attacks on Blockchains
- ReMlX: Resilience for ML Ensembles using XAI at Inference against Faulty Training Data
- D-semble: Efficient Diversity-Guided Search for Resilient ML Ensembles
- A Method to Facilitate Membership Inference Attacks in Deep Learning Models
- SAM: Foreseeing Inference-Time False Data Injection Attacks on ML-enabled Medical Devices
- AutoPatch: Automated Generation of Hotpatches for Real-Time Embedded Devices
- SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attacks
- Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models
Pages
- About us
- Awards
- Papers
- People
- Photos
- Projects
- Autonomous Systems and IoT Security
- Building Robust ML Systems against Training Data Faults
- Decentralized Finance (DeFi) and Blockchain Oracle Security
- Error Resilient ML Applications
- Membership Inference Attacks in Machine Learning Models
- Middleware for Edge Computing Applications
- Resilience Assessment of ML Models under Hardware Faults
- Smart Contract’s Security
- Software