초록

공격 발생시에 네트워크에 나타나는 징후정보를 수집하여 공격을 분류하는 징후기반공격분류법을 제안한다. 이 공격분류법은 공격 발생시 징후를 이용하므로 필요한 정보의 수집이 빠르고 알려지지 않은 공격에 대한 분류가 가능한 장점이 있다. 제안하는 공격법은 두 단계로 공격을 분류한다. 단일 공격자로부터 단일 공격대상에게 나타나는 단일 공격들을 먼저 분류하고 단일 공격들이 서로 연관성 없는 다른 공격들인지 아니면 동일한 하나의 공격을 구성하는 연관된 공격인지 판단하게 된다. 따라서, 이미 제안된 공격분류법보다 정확하게 분산서비스거부공격이나 웜, Bot과 같은 공격을 분류할 수 있게 되었다. 제안하는 분류법을 이용하여 원과 분산서비스거부공격의 특정 및 근거리통신망에서 발생하는 공격의 특정을 도출하였고 이러한 특정들은 새로운 웜이나 분산서비스거부공격 또는 근거리통신망에서 발생하는 공격들도 공통적으로 가지는 특정임을 보였다.

Abstract

We present a symptom based taxonomy for network security. This taxonomy classifies attacks in the network using early symptoms of the attacks. Since we use the symptom it is relatively easy to access the information to classify the attack. Furthermore we are able to classify the unknown attack because the symptoms of unknown attacks are correlated with the one of known attacks. The taxonomy classifies the attack in two stages. In the first stage, the taxonomy identifies the attack in a single connection and then, combines the single connections into the aggregated connections to check if the attacks among single connections may create the distribute attack over the aggregated connections. Hence, it is possible to attain the high accuracy in identifying such complex attacks as DDoS, Worm and Bot We demonstrate the classification of the three major attacks in Internet using the proposed taxonomy.

주제어

#네트워크 공격   #징후   #공격분류법   #네트워크 보안  

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