스텍앙상블과 인접 넷플로우를 활용한 침입 탐지 시스템
Intrusion Detection System Utilizing Stack Ensemble and Adjacent Netflow
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초록
본 논문은 네트워크에서 침입 행위를 하는 플로우를 탐지하는 네트워크 침입 탐지 시스템을 제안한다. 대다수 연구에 활용되는 데이터세트는 시계열 정보를 포함하고 있지 않으며, 공격 사례가 적은 공격은 샘플 데이터 수가 부족해 탐지율 향상이 어렵다. 하지만 탐지 방안에 대해 연구 결과가 부족한 상황이다. 본 연구에서는 ANN(Artificial Neural Network) 모델과 스택 앙상블 기법을 활용한 선행 연구를 토대로 하였다. 앞서 언급한 문제점을 해결하기 위해 인접 플로우를 활용하여 시계열 정보를 추가하고 희소 공격의 샘플을 강화하여 학습하여 탐지율을 보강하였다.
Abstract
This paper proposes a network intrusion detection system that identifies abnormal flows within the network. The majority of datasets commonly used in research lack time-series information, making it challenging to improve detection rates for attacks with fewer instances due to a scarcity of sample data. However, there is insufficient research regarding detection approaches. In this study, we build upon previous research by using the Artificial neural network(ANN) model and a stack ensemble technique in our approach. To address the aforementioned issues, we incorporate temporal information by leveraging adjacent flows and enhance the learning of samples from sparse attacks, thereby improving both the overall detection rate and the detection rate for sparse attacks.
참고문헌 (16)
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