Disentangled Dynamic Intrusion Detection

• URL: http://arxiv.org/abs/2307.11079v2
• Date: Sat, 14 Dec 2024 09:12:39 GMT
• Title: Disentangled Dynamic Intrusion Detection
• Authors: Chenyang Qiu, Guoshun Nan, Hongrui Xia, Zheng Weng, Xueting Wang, Meng Shen, Xiaofeng Tao, Jun Liu,
• Abstract summary: We propose DIDS-MFL, a disentangled intrusion detection method to handle various intrusion detection scenarios.<n>DIDS-MFL involves two key components: a double Disentanglementbased Intrusion Detection System (DIDS) and a plug-and-play Multi-scale Few-shot Learning-based (MFL) intrusion detection module.
• Score: 17.155894470599762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network-based intrusion detection system (NIDS) monitors network traffic for malicious activities, forming the frontline defense against increasing attacks over information infrastructures. Although promising, our quantitative analysis shows that existing methods perform inconsistently in declaring various attacks, and perform poorly in few-shot intrusion detections. We reveal that the underlying cause is entangled distributions of flow features. This motivates us to propose DIDS-MFL, a disentangled intrusion detection method to handle various intrusion detection scenarios. DIDS-MFL involves two key components, respectively: a double Disentanglementbased Intrusion Detection System (DIDS) and a plug-and-play Multi-scale Few-shot Learning-based (MFL) intrusion detection module. Specifically, the proposed DIDS first disentangles traffic features by a non-parameterized optimization, automatically differentiating tens and hundreds of complex features of various attacks. Such differentiated features will be further disentangled to highlight the attack-specific features. Our DIDS additionally uses a novel graph diffusion method that dynamically fuses the network topology in evolving data streams. Furthermore, the proposed MFL involves an alternating optimization framework to address the entangled representations in few-shot traffic threats with rigorous derivation. MFL first captures multiscale information in latent space to distinguish attack-specific information and then optimizes the disentanglement term to highlight the attack-specific information. Finally, MFL fuses and alternately solves them in an end-to-end way. Experiments show the superiority of our proposed DIDS-MFL. Our code is available at https://github.com/qcydm/DIDS-MFL

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