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Journal of Applied Sciences
Year: 2006  |  Volume: 6  |  Issue: 6  |  Page No.: 1248 - 1250

Finite State Machine Detection Model Based on Adaptive TTL Neuron

Zhang Jun and Gao Lei    

Abstract: Without detailed knowledge of the network topology between Network Security Audit System (NSAS) and the end system, NSAS may be unable to determine whether a given packet will even be seen by the end system. Finite state machine detection model based on adaptive TTL neuron is proposed to solve it in this paper. By inspecting the variance of Time To Live (TTL) field in ingress sequence, the adaptive TTL neuron compares the TTL field of new fragment with the average to detect whether the TTL field of new fragment is normal or not. Combining the TTL neuron with the detection state node of finite state machine detection model, ingress flow will be benign and NSAS won`t be confused anymore.

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