{"title":"深度图流SVDD:网络物理系统中的异常检测","authors":"Ehtesamul Azim, Dongjie Wang, Yanjie Fu","doi":"10.48550/arXiv.2302.12918","DOIUrl":null,"url":null,"abstract":"Our work focuses on anomaly detection in cyber-physical systems. Prior literature has three limitations: (1) Failing to capture long-delayed patterns in system anomalies; (2) Ignoring dynamic changes in sensor connections; (3) The curse of high-dimensional data samples. These limit the detection performance and usefulness of existing works. To address them, we propose a new approach called deep graph stream support vector data description (SVDD) for anomaly detection. Specifically, we first use a transformer to preserve both short and long temporal patterns of monitoring data in temporal embeddings. Then we cluster these embeddings according to sensor type and utilize them to estimate the change in connectivity between various sensors to construct a new weighted graph. The temporal embeddings are mapped to the new graph as node attributes to form weighted attributed graph. We input the graph into a variational graph auto-encoder model to learn final spatio-temporal representation. Finally, we learn a hypersphere that encompasses normal embeddings and predict the system status by calculating the distances between the hypersphere and data samples. Extensive experiments validate the superiority of our model, which improves F1-score by 35.87%, AUC by 19.32%, while being 32 times faster than the best baseline at training and inference.","PeriodicalId":91995,"journal":{"name":"Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings. Part I. Pacific-Asia Conference on Knowledge Discovery and Data Mining (21st : 2017 : Cheju Isl...","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems\",\"authors\":\"Ehtesamul Azim, Dongjie Wang, Yanjie Fu\",\"doi\":\"10.48550/arXiv.2302.12918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Our work focuses on anomaly detection in cyber-physical systems. Prior literature has three limitations: (1) Failing to capture long-delayed patterns in system anomalies; (2) Ignoring dynamic changes in sensor connections; (3) The curse of high-dimensional data samples. These limit the detection performance and usefulness of existing works. To address them, we propose a new approach called deep graph stream support vector data description (SVDD) for anomaly detection. Specifically, we first use a transformer to preserve both short and long temporal patterns of monitoring data in temporal embeddings. Then we cluster these embeddings according to sensor type and utilize them to estimate the change in connectivity between various sensors to construct a new weighted graph. The temporal embeddings are mapped to the new graph as node attributes to form weighted attributed graph. We input the graph into a variational graph auto-encoder model to learn final spatio-temporal representation. Finally, we learn a hypersphere that encompasses normal embeddings and predict the system status by calculating the distances between the hypersphere and data samples. Extensive experiments validate the superiority of our model, which improves F1-score by 35.87%, AUC by 19.32%, while being 32 times faster than the best baseline at training and inference.\",\"PeriodicalId\":91995,\"journal\":{\"name\":\"Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings. Part I. Pacific-Asia Conference on Knowledge Discovery and Data Mining (21st : 2017 : Cheju Isl...\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings. Part I. 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Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems
Our work focuses on anomaly detection in cyber-physical systems. Prior literature has three limitations: (1) Failing to capture long-delayed patterns in system anomalies; (2) Ignoring dynamic changes in sensor connections; (3) The curse of high-dimensional data samples. These limit the detection performance and usefulness of existing works. To address them, we propose a new approach called deep graph stream support vector data description (SVDD) for anomaly detection. Specifically, we first use a transformer to preserve both short and long temporal patterns of monitoring data in temporal embeddings. Then we cluster these embeddings according to sensor type and utilize them to estimate the change in connectivity between various sensors to construct a new weighted graph. The temporal embeddings are mapped to the new graph as node attributes to form weighted attributed graph. We input the graph into a variational graph auto-encoder model to learn final spatio-temporal representation. Finally, we learn a hypersphere that encompasses normal embeddings and predict the system status by calculating the distances between the hypersphere and data samples. Extensive experiments validate the superiority of our model, which improves F1-score by 35.87%, AUC by 19.32%, while being 32 times faster than the best baseline at training and inference.