卷积神经网络与混合专家模型用于5G及未来网络入侵检测
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
AI总结：
本文提出结合CNN与稀疏门控MoE的5G及未来网络入侵检测方法，在真实5G-NIDD数据集上加权F1分数达99.95%。
摘要：
6G/NextG网络的出现带来了一系列优势，包括极高的容量、可靠性和效率。然而，这些网络可能容易受到新型安全威胁的影响。因此，6G/NextG网络必须配备先进的人工智能算法，以规避这些攻击。现有关于入侵检测任务的研究依赖于一系列浅层机器学习分类器，包括逻辑回归、决策树等，导致性能欠佳。另一些研究则基于由静态组件构成的深度神经网络，这些组件不会根据输入进行条件调整，限制了其表示能力和效率。为解决这些问题，我们提出首个将Mixture of Experts（MoE，混合专家）用于识别恶意流量的研究。具体而言，我们使用网络流量数据，并将一维特征数组转换为二维矩阵。随后，将该矩阵输入卷积神经网络（CNN）层，之后接批量归一化和最大池化层。在通过CNN层获得表示向量后，使用稀疏门控MoE层。该层由一组专家（全连接层）和一个路由器组成，路由器为每个专家的输出分配权重。稀疏性通过从全部专家中选择最相关的专家来实现。最后，我们开展一系列消融实验以证明所提模型的有效性。实验在5G-NIDD数据集上进行，该数据集是从真实5G测试网络生成的网络入侵检测数据集。结果表明，我们提出的方法加权F1分数最高达到99.95%，取得了与现有方法相当的性能。研究结果还显示，与最先进方法相比，所提模型具有多项优势。
英文摘要：
The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new security threats. Therefore, 6G/NextG networks must be equipped with advanced Artificial Intelligence algorithms, in order to evade these attacks. Existing studies on the intrusion detection task rely on the train of shallow machine learning classifiers, including Logistic Regression, Decision Trees, and so on, yielding suboptimal performance. Others are based on deep neural networks consisting of static components, which are not conditional on the input. This limits their representation power and efficiency. To resolve these issues, we present the first study integrating Mixture of Experts (MoE) for identifying malicious traffic. Specifically, we use network traffic data and convert the 1D array of features into a 2D matrix. Next, we pass this matrix through convolutional neural network (CNN) layers followed by batch normalization and max pooling layers. After obtaining the representation vector via the CNN layers, a sparsely gated MoE layer is used. This layer consists of a set of experts (dense layers) and a router, where the router assigns weights to the output of each expert. Sparsity is achieved by choosing the most relevant experts of the total ones. Finally, we perform a series of ablation experiments to prove the effectiveness of our proposed model. Experiments are conducted on the 5G-NIDD dataset, a network intrusion detection dataset generated from a real 5G test network. Results show that our introduced approach reaches weighted F1-score up to 99.95% achieving comparable performance to existing approaches. Findings also show that our proposed model achieves multiple advantages over state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
