Comprehensive Java Metadata Tracking for Attack Detection and Repair
Name
ClearTrack_mit_tech_report.pdf
Description
Accepted version
Size
193.25 KB
Format
Adobe PDF
Checksum (MD5)
4fcf9f3b6ae49456d7233bbda18a29da
Author(s)
Perkins, Jeff
Eikenberry, Jordan
Coglio, Allessandro
Rinard, Martin
Date Issued
June 2020
Journal
Proceedings - 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2020
Publisher
IEEE
Citation
Perkins, Jeff, Eikenberry, Jordan, Coglio, Allessandro and Rinard, Martin. 2020. "Comprehensive Java Metadata Tracking for Attack Detection and Repair." Proceedings - 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2020.
Version
Author's final manuscript
Abstract
© 2020 IEEE. We present ClearTrack, a system that tracks meta-data for each primitive value in Java programs to detect and nullify a range of vulnerabilities such as integer overflow/underflow and SQL/command injection vulnerabilities. Contributions include new techniques for eliminating false positives associated with benign integer overflows and underflows, new metadata-aware techniques for detecting and nullifying SQL/command command injection attacks, and results from an independent evaluation team. These results show that 1) ClearTrack operates successfully on Java programs comprising hundreds of thousands of lines of code (including instrumented jar files and Java system libraries, the majority of the applications comprise over 3 million lines of code), 2) because of computations such as cryptography and hash table calculations, these applications perform millions of benign integer overflows and underflows, and 3) ClearTrack successfully detects and nullifies all tested integer overflow and underflow and SQL/command injection vulnerabilities in the benchmark applications.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/dsn48063.2020.00024
