Comparer des méthodes

	La cryptanalyse différentielle	HMAC	Analyse de la souillure
Domaine	Cryptographie	Cryptographie	Cryptographie
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1990	1997	2005
Auteur d'origine	Eli Biham	Hugo Krawczyk	James Newsome
Type	statistical attack on block ciphers	cryptographic authentication mechanism	data flow tracking technique
Source fondatrice	Biham, E., & Shamir, A. (1990). Differential cryptanalysis of DES-like cryptosystems. In Advances in Cryptology - CRYPTO 1990, LNCS 537, pp. 2-21.	Krawczyk, H., Bellare, M., & Crechanko, R. (1997). HMAC: Keyed-Hashing for Message Authentication. RFC 2104.	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).
Alias	differential attack, differential path, differential probability	HMAC, keyed hash function	taint analysis, information flow, data tainting
Apparentées	3	3	3
Résumé	Differential cryptanalysis is a statistical attack technique on symmetric block ciphers that analyzes differences in inputs and outputs to recover secret keys. Introduced by Eli Biham and Adi Shamir in 1990, differential cryptanalysis was the first practical attack on DES that outperformed brute force search. The technique exploits non-random properties of cipher transformations by studying how small changes in plaintext propagate through the cipher rounds. Differential cryptanalysis has shaped cipher design for three decades.	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.