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| HMAC× | Taint-analyse× | |
|---|---|---|
| Fagområde | Kryptografi | Kryptografi |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 1997 | 2005 |
| Ophavsperson≠ | Hugo Krawczyk | James Newsome |
| Type≠ | cryptographic authentication mechanism | data flow tracking technique |
| Oprindelig kilde≠ | Krawczyk, H., Bellare, M., & Crechanko, R. (1997). HMAC: Keyed-Hashing for Message Authentication. RFC 2104. link ↗ | Newsome, J., & Song, D. X. (2005). Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on commodity software. In Network and Distributed System Security Symposium (NDSS 2005). link ↗ |
| Aliasser≠ | HMAC, keyed hash function | taint analysis, information flow, data tainting |
| Relaterede | 3 | 3 |
| Resumé≠ | HMAC (Hash-Based Message Authentication Code) is a cryptographic algorithm for authenticating messages using a secret key and a hash function. Standardized in RFC 2104 (1997), HMAC can be combined with any cryptographic hash function (SHA-256, SHA-3, etc.) to create a message authentication code (MAC). HMAC provides both data integrity and authentication, detecting both accidental corruption and deliberate tampering, and is widely used in web security (TLS/SSL), API authentication, and network protocols. | Taint analysis is a data flow analysis technique that tracks how untrusted (tainted) input flows through a program to identify vulnerabilities where tainted data reaches dangerous operations (sinks). Formalized by Newsome and Song in 2005, taint analysis marks input data as tainted and propagates taint labels through the program, alerting when tainted data reaches sensitive operations like SQL queries or system calls. Taint analysis is fundamental to detecting injection vulnerabilities and is widely used in dynamic analysis tools and security monitoring systems. |
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