Graph neural networksanomaly detectionhuman cyber physical systemssoftware defined networkingHuman Cyber Physical Systems (HCPS) encompass humans, networks, and physical devices, characterized by intricate interactions and interdependencies. The complexity of HCPS networks makes them vulnerable to network issues...
一是不同传感器之间有着非常不同的行为,即图中节点的数值和分布差异很大,因此需要考虑如何对传感器,即图中节点进行特征表示;二是GNNs的输入必须是整个图,即包括图中节点的特征表示以及各节点的连接关系,而在本文场景中,各节点之间的关系都是未知的(以往的方法是直接认为各节点之间都存在关系,即使用完全图表征各节点...
论文链接:[2106.06947v1] Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (arxiv.org) 主要内容 论文提出了一种图偏差网络(GDN)框架用于多元时间序列异常检测,该框架可以实验对一个系统中各个传感器之间的结构关系的建模,传感器异常检测以及异常传感器的定位等,通过两个水处理厂的数据集进行...
Xiao et al. [11] proposed a graph embedding approach to perform anomaly detection on network flows. The authors first converted the network flows into a first-order and secondorder graph. The first-order graph learns the latent features from the perspective of a single host by using its IP ...
smoothedScore = movmean(scorePerTime,windowSize);end References [1] A. Deng and B. Hooi, “Graph neural network-based anomaly detection in multivariate time series,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021....
Code implementation for :Graph Neural Network-Based Anomaly Detection in Multivariate Time Series(AAAI'21) Installation Python >= 3.6 cuda == 10.2 Pytorch==1.5.1 PyG: torch-geometric==1.5.0 Install packages # run after installing correct Pytorch package bash install.sh ...
ASA-GNN: adaptive sampling and aggregation-based graph neural network for transaction fraud detection. IEEE Transactions on Computational Social Systems, 2024, 11(3): 3536–3549 Article MATH Google Scholar Sun H, Liu Z, Wang S, Wang H. Adaptive attention-based graph representation learning to ...
Xiao et al. [11] proposed a graph embedding approach to perform anomaly detection on network flows. The authors first converted the network flows into a first-order and secondorder graph. The first-order graph learns the latent features from the perspective of a single host by using its IP ...
回顾了异常检测(Anomaly Detection)、多元时间序列数据模型 (models for multivariate time series data)、图神经网络(Graph neural network)的研究相关工作,并指出其不足。 2.1 异常检测(Anomaly Detection) 目的是检测出偏离大部分数据的异常样本,经典方法包括基于密度的研究方法、基于线性模型的研究方法、基于距离的研究...
competitive graph neural network (CGNN)-based fraud detection system (eFraudCom) 一种基于竞争图神经网络(CGNN)的欺诈检测系统(eFraudCom),以检测电子商务平台上的欺诈行为。CGNN是一个基于GCN的GAE系统。eFraudCom系统由数据处理器和欺诈检测器组成。具体而言,在数据处理器中,对代表性的正态数据进行采样,并...