5G/LTE 네트워크에서의 DBSCAN 클러스터링 기반 시그널링 공격 탐지
DBSCAN Clustering-Based Detection of Signaling Attack in 5G/LTE Networks
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초록
5G 이동통신망은 기존 스마트폰 위주였던 LTE와는 다르게 수많은 단말과 애플리케이션에 서비스를 제공하고 있다. 이러한 5G의 초저지연 특성, 대량 단말 수용으로의 변화는 시그널링 구간의 오버헤드를 증가시키고, 다양해진 트래픽 패턴으로 인해 악성 단말을 탐지하기 더욱 어려워졌다. 본 연구에서는 시그널링 구간을 표적으로 하는 Denial of Service(DoS) 공격의 일종인 시그널링 공격 탐지 방법을 제안한다. 비지도 학습 기반의 DBSCAN 클러스터링은 다양한 패턴으로 분포된 단말을 클러스터링 할 수 있으며, 과거 데이터에 의존하지 않기 때문에 다양한 유형의 트래픽에 유연하게 대응할 수 있다. 또한 탐지모델의 실제 환경에서의 활용 가능성을 판단하기 위해 네트워크에서 실시간으로 수집한 데이터를 활용했으며, 실험 결과 99.32%의 탐지 정확도(accuracy)와 0.03%의 오 차단율(false positive rate)로 기존 제안보다 우수한 성능을 보여준다.
Abstract
The 5G mobile network provides various services to numerous devices and applications, unlike LTE which focuses on smartphones. Features of 5G, such as low latency and massive connectivity, increase the overhead of the control plane(CP, signaling part) and make it difficult to detect abnormal devices due to random traffic patterns. In this paper, we propose a DBSCAN clustering-based detection method to counter signaling attacks, which are a type of 'Denial of Service(DoS)' attack targeting mobile networks. DBSCAN helps to create clusters of various shapes and can address dynamic traffic because the algorithm needs not to depend on past traffic statistics. We also use a real-time traced dataset for experiments to assess usability in real-world scenarios. According to the experiments, our method achieves 99.32% of accuracy and 0.03% of false-positive rates, demonstrating superior performance compared to previous works.
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