Incremental-learning-based graph neural networks on edge-forwarding devices for network intrusion detection

IF 6.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Qiang Gao , Samina Kausar , HuaXiong Zhang
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引用次数: 0

Abstract

Graph neural networks have become one of the research hotspots for network intrusion detection due to their natural suitability for representing computer networks. However, most of the related research on training GNNs is centralized, and this approach involves long-distance transmission and dumping of network data, so it is inefficient to perform, has the potential for privacy leakage, and introduces an additional transmission burden to the network. To address these challenges, this paper investigates the feasibility of offloading both graph neural networks' training and inference phases to edge-forwarding devices such as switches. We propose a distributed framework that aggregates residual computational resources from edge-forwarding devices into a micro-computing network. This framework then migrates GNN execution to edge-forwarding devices through a hybrid parallelism paradigm, thus locally detecting network anomalies to reduce network data transmission significantly. Meanwhile, to address the problem of computational and memory constraints of edge-forwarding devices, we propose a novel attention heatmap-driven memoryless incremental learning algorithm that learns network features and detects anomalies with minimal resources while avoiding catastrophic forgetting. Finally, we implement and verify the feasibility of the above framework and algorithm using a general-purpose embedded system and open-source software. The experiments show that although each edge-forwarding device's computational and memory load is light, the framework performs similarly to traditional approaches. To the best of our knowledge, this is the first approach that offloads a graph neural network model to edge-forwarding devices.
边缘转发设备上基于增量学习的图神经网络用于网络入侵检测
图神经网络以其天然的适合表示计算机网络的特性,成为网络入侵检测的研究热点之一。然而,大多数训练gnn的相关研究都是集中式的,这种方法涉及网络数据的远距离传输和转储,执行效率低,存在隐私泄露的可能性,并且给网络带来了额外的传输负担。为了解决这些挑战,本文研究了将图神经网络的训练和推理阶段卸载到边缘转发设备(如交换机)的可行性。我们提出了一个分布式框架,将边缘转发设备的剩余计算资源聚合到一个微计算网络中。然后,该框架通过混合并行范式将GNN执行迁移到边缘转发设备,从而在本地检测网络异常,从而显着减少网络数据传输。同时,为了解决边缘转发设备的计算和内存限制问题,我们提出了一种新的注意力热图驱动的无记忆增量学习算法,该算法在避免灾难性遗忘的同时,以最小的资源学习网络特征并检测异常。最后,利用通用嵌入式系统和开源软件实现并验证了上述框架和算法的可行性。实验表明,尽管每个边缘转发设备的计算和内存负载都很轻,但该框架的性能与传统方法相似。据我们所知,这是第一个将图神经网络模型卸载到边缘转发设备的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
alexandria engineering journal
alexandria engineering journal Engineering-General Engineering
CiteScore
11.20
自引率
4.40%
发文量
1015
审稿时长
43 days
期刊介绍: Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification: • Mechanical, Production, Marine and Textile Engineering • Electrical Engineering, Computer Science and Nuclear Engineering • Civil and Architecture Engineering • Chemical Engineering and Applied Sciences • Environmental Engineering
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