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Título: =Self-addressable memory-based FSM: a scalable intrusion detection engine
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Publicación seriada
Referencias AnalíticasReferencias Analíticas
Autor: Soewito, Benfano
Título: Self-addressable memory-based FSM: a scalable intrusion detection engine
Páginas/Colación: pp. 14-21
Fecha: Vol. 23, n. 1
IEEE Network Vol. 23, no. 1 Jan./Feb. 2009
Información de existenciaInformación de existencia

Resumen
One way to detect and thwart a network attack is to compare each incoming packet with predefined patterns, also called an attack pattern database, and raise an alert upon detecting a match. This article presents a novel pattern-matching engine that exploits a memory-based, programmable state machine to achieve deterministic processing rates that are independent of packet and pattern characteristics. Our engine is a self-addressable memory-based finite state machine (SAMFSM), whose current state coding exhibits all its possible next states. Moreover, it is fully reconfigurable in that new attack patterns can be updated easily. A methodology was developed to program the memory and logic. Specifically, we merge "non-equivalent" states by introducing "super characters" on their inputs to further enhance memory efficiency without adding labels. SAM-FSM is one of the most storage-efficient machines and reduces the memory requirement by 60 times. Experimental results are presented to demonstrate the validity of SAM-FSM.

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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