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利用图神经网络在网络安全中实现强大的 DDoS 攻击检测.pdf

上传人: 芦苇 编号:186048 2024-11-02 28页 2.52MB

1、CONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.Kartikeya SharmaSenior Associate Information Security Engineer at EquinixCONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What ar

2、e Graph Neural Networks?CONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What are Graph Neural Networks?Graph Neural Networks are powerful AI tools that learn from connected data,helping us uncover hidden patterns in complex networks.CONFI

3、DENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What are Graph Neural Networks?Nodes(also known as vertices)represent entities or objects in a graph.Edgesrepresent the relationships or connections between nodes.CONFIDENTIALITY NOTICE:This docu

4、ment is intended only for the use of the recipients to which Equinix sends it.What are Graph Neural Networks?GNNs learn rich node representations,calledembeddings using Message PassingCONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What a

5、re Graph Neural Networks?GNNs have found applications in various domains,including:Social network analysis Molecular property prediction Knowledge graph completion Recommender systemsCONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.GNNs vs

6、 Traditional Neural NetworksAspectGraph Neural NetworksTraditional Neural NetworksInput StructureGraphs with variable size and connectivityFixed-size,grid-like input(e.g.,images,sequences)RelationshipsModels and learns from relationships between entitiesAssumes independence between input featuresNod

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本文主要介绍了图神经网络(GNN)和分布式拒绝服务(DDoS)攻击检测。GNN是一种强大的AI工具,能够从连接的数据中学习,帮助我们揭示复杂网络中的隐藏模式。在GNN中,节点(也称为顶点)代表实体或对象,边代表节点之间的关系。GNN已经应用于包括社交网络分析、分子属性预测、知识图谱补全和推荐系统在内的多个领域。与传统的神经网络相比,GNN在处理图结构数据时有其独特的优势,如自动特征学习、建模复杂关系等,但也存在计算复杂度高和解释性挑战等问题。在DDoS攻击检测中,GNN通过将网络表示为图,并学习节点和边的嵌入来检测恶意活动。与传统方法相比,GNN在自动特征学习和处理复杂关系方面具有优势,但在计算复杂度和解释性方面面临挑战。文章还提到了使用数据包和流量作为节点的方法,并比较了不同数据集上的准确性和性能。
"GNN是什么?" "DDoS攻击如何检测?" "如何使用数据包作为节点来建模网络?"
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