Publisher
Springer Science and Business Media LLC
Reference112 articles.
1. Muandet K, Schölkopf B. One-class support measure machines for group anomaly detection. 2013. arXiv preprint arXiv:1303.0309.
2. Tong H, Papadimitriou S, Sun J, Yu PS, Faloutsos C. Colibri: fast mining of large static and dynamic graphs. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, p. 686–94. ACM; 2008.
3. Kuppa A, Grzonkowski S, Asghar MR, Le-Khac NA. Finding rats in cats: detecting stealthy attacks using group anomaly detection. In: 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), p. 442–449. IEEE; 2019.
4. He Z, Xu X, Deng S. Discovering cluster-based local outliers. Pattern Recogn Lett. 2003;24(9):1641–50.
5. Eberle W, Holder L, Massengill B. Graph-based anomaly detection applied to homeland security cargo screening. In: Twenty-Fifth International FLAIRS Conference. 2012.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Healthcare insurance fraud detection using data mining;BMC Medical Informatics and Decision Making;2024-04-26
2. A Comprehensive Review of Deep Anomaly Detection Techniques- An Analysis;2024 IEEE 9th International Conference for Convergence in Technology (I2CT);2024-04-05
3. CNN-BiLSTM based GAN for Anamoly Detection from Multivariate Time Series Data;2023 24th International Conference on Digital Signal Processing (DSP);2023-06-11
4. Artificial intelligence-based approach for Univariate time-series Anomaly detection using Hybrid CNN-BiLSTM Model;2022 13th International Conference on Electrical Engineering (ICEENG);2022-03-29