방법 비교

	패킷 심층 분석	HMAC	오염 분석 (Taint Analysis)
분야	암호학	암호학	암호학
계열	Machine learning	Machine learning	Machine learning
기원 연도	1990s	1997	2005
창시자	Unknown	Hugo Krawczyk	James Newsome
유형	packet inspection technique	cryptographic authentication mechanism	data flow tracking technique
원전	Leconte, M., & Thomas, A. (2009). Deep Packet Inspection (DPI) technologies. In Proceedings of the Global Telecommunications Conference (GLOBECOM), 2009, pp. 1-6.	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).
별칭	DPI, complete packet inspection, packet filtering	HMAC, keyed hash function	taint analysis, information flow, data tainting
관련	3	3	3
요약	Deep Packet Inspection (DPI) is a network traffic analysis technique that examines the complete packet payload beyond header information to identify, classify, and potentially control data traffic. Developed in the 1990s for network monitoring and management, DPI analyzes packet contents to detect protocols, applications, and patterns, enabling security monitoring, quality of service management, and content filtering. DPI is widely used by Internet service providers, enterprises, and security organizations to monitor network traffic and enforce policies.	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.