Manticore: An Unsupervised Intrusion Detection System Based on Contrastive Learning in 5G Networks

Lu Yuan, Jiyan Sun, Shangyuan Zhuang, Yinlong Liu, Liru Geng, Jing Zou, Peizhe Xin, Weiqing Huang, Wei Ma

Abstract: The increasing complexity and openness of 5G networks naturally enlarge the attack surface and introduce new vulnerabilities, thereby posing challenges to the performance of existing intrusion detection systems (IDSs). Current IDSs solely rely on statistical features, which may suffer from low accuracy due to the complex traffic patterns in 5G networks. Additionally, recent IDSs apply contrastive learning to improve detection capabilities, but the reliance on costly manual labeling hinders the adaptability to complex attacks in 5G networks.In this paper, we present Manticore, an unsupervised intrusion detection system based on contrastive learning for 5G networks. Specifically, Manticore leverages both statistical features and original features of packets to capture the holistic information of traffic in 5G networks. Moreover, it automatically establishes positive and negative pairs without manual labeling. We further explore the combination patterns between reconstruction loss and contrastive loss to attain a more precise model. Our experimental evaluation of two datasets demonstrates the proposed Manticore outperforms the relevant state-of-the-art methods.