Tag Archives: conference

PID-Piper: Recovering Robotic Vehicles from Physical Attacks

Pritam Dash, Guanpeng Li, Zitao Chen, Mehdi Karimibiuki, and Karthik Pattabiraman, To appear in the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2021. (Acceptance Rate: 16.5%). [ PDF | Talk ] (Code, Videos) Best Paper Award Candidate (1 of 3).
Continue reading

Comments Off on PID-Piper: Recovering Robotic Vehicles from Physical Attacks

Filed under papers

A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Zitao Chen, Guanpeng Li, and Karthik Pattabiraman, To appear in the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2021. (Acceptance Rate: 16.5%). [ PDF | Talk ] (arXIV, code) Best Paper Award Candidate (1 of 3).
Continue reading

Comments Off on A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Filed under papers

How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method

Yong Yang, Yifan Yu, Karthik Pattabiraman, Long Wang, Ying Li, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2020. (Acceptance Rate: 26%). [ PDF | Talk ] (Code)
Continue reading

Comments Off on How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method

Filed under papers

TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Zitao Chen, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2020. (Acceptance Rate: 26%) [ PDF | Talk ] (Code)
Continue reading

Comments Off on TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Filed under papers

GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs

Abdul Rehman Anwer, Guanpeng Li, Karthik Pattabiraman, Michael Sullivan, Timothy Tsai and Siva Hari, ACM International Conference on High-Performance Computing, Networking, Storage, and Analyzis (SC), 2020 (Acceptance Rate: 25.1%) [PDF | Talk] (Code)
Continue reading

Comments Off on GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs

Filed under papers

How Effective are Smart Contract Static Analysis Tools ? Evaluating Smart Contract Static Analysis Tools Using Bug Injection

Asem Ghaleb and Karthik Pattabiraman, Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), 2020. (Acceptance Rate: 26%) [PDF | Talk] (DataSet, CodeArtifacts Functional Badge)
Continue reading

Comments Off on How Effective are Smart Contract Static Analysis Tools ? Evaluating Smart Contract Static Analysis Tools Using Bug Injection

Filed under papers

TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis

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)
Continue reading

Comments Off on TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis

Filed under papers

Out of Control: Stealthy Attacks on Robotic Vehicles Protected by Control-Based Techniques

Pritam Dash, Mehdi Karimibiuki, and Karthik Pattabiraman, Annual Computer Security Applications Conference (ACSAC), 2019. (Acceptance Rate: 22.6%) [ PDF | Talk ] (CodeArtifacts Reusable Badge from ACM)(Videos) This work appeared in the media (Eureka alert)(TechXplore)(Globalnews)(Market Associates)(Helpnet, SERENE-RISC digest)
Continue reading

Comments Off on Out of Control: Stealthy Attacks on Robotic Vehicles Protected by Control-Based Techniques

Filed under papers

A Tale of Two Injectors: End-to-End Comparison of IR-level and Assembly-Level Fault Injection

Lucas Palazzi, Guanpeng Li, Bo Fang, and Karthik Pattabiraman, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2019. (Acceptance Rate: 31.4%) [ PDF | Talk ] (code)
Continue reading

Comments Off on A Tale of Two Injectors: End-to-End Comparison of IR-level and Assembly-Level Fault Injection

Filed under papers

BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems

Zitao Chen, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2019. (Acceptance Rate: 21%) [ PDF | Talk ] ( Code Finalist for the SC reproducibility challenge (one of 3 papers))
Continue reading

Comments Off on BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems

Filed under papers