Dytan: a generic dynamic taint analysis framework

By J. Clause, Wanchun Li, Alessandro Orso. International Symposium on Software Testing and Analysis, 2007

600 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/66d00613ad789411c8a37a5392354c078d0d27b2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ahmad-Reza Sadeghi
2. Lucas Davi
3. Wenke Lee
4. Koushik Sen
5. Dawn Song
6. Guofei Gu
7. Roberto Perdisci
8. David Brumley

In Information Systems

1. Koushik Sen
2. Wenke Lee
3. David Brumley
4. Thanassis Avgerinos
5. Roberto Perdisci
6. Guofei Gu
7. Edward J. Schwartz
8. Dawn Song

In Signal Processing

1. Ahmad-Reza Sadeghi
2. Lucas Davi
3. Roberto Perdisci
4. Koushik Sen
5. David Brumley
6. Thanassis Avgerinos
7. Edward J. Schwartz
8. Wenke Lee

Citations in the corpus

• Fides: Towards a Platform for Responsible Data Science (2017, neutral): “manner similar to taint analysis for secure Web programming [13].”
• TaintScope: A Checksum-Aware Directed Fuzzing Tool for Automatic Software Vulnerability Detection (2010, background): “Extending TaintScope to track control flow propagation (similar to [37] and DYTAN [29]) and improving data flow propagation are parts of our future work.”
• ROPdefender: a detection tool to defend against return-oriented programming attacks (2011, methodology): “Moreover, it has been used in [61] for a checksum-aware fuzzing tool and in [17] as dynamic taint analysis system.”
• DTA++: Dynamic Taint Analysis with Targeted Control-Flow Propagation (2011, methodology): “In particular, we concentrate on the common case of informationpreserving transformations in benign programs.”
• All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask) (2010, background): “and thus can be used to compute control-dependent taint [1, 20, 52].”
• Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks (2018, methodology): “While taint tracking is a well-known technique to detect leakage of private data [19] or memory corruption attacks [13], we apply it for the first time to a smart contract execution platform.”
• Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces (2010, positive): “formed to send or receive information critical for the correct functioning of the malware using dynamic taint analysis [13].”
• Jalangi: a selective record-replay and dynamic analysis framework for JavaScript (2013, background): “• Dynamic taint analysis [21, 9]: a dynamic taint analysis is a form of information flow analysis which checks if information can flow from a specific set of memory locations, called sources, to another set of memory locations, called sinks.”
• Detecting trigger-based behaviors in botnet malware (2015, background): “In order to mitigate the problem, symbol propagation through implicit control flow could be considered [2-5 , 7].”