Similar articles for PMID: 29994493

Year	Number of Results
1996	1
1999	1
2003	1
2005	1
2008	1
2009	2
2010	4
2011	5
2012	3
2013	3
2014	3
2015	13
2016	10
2017	21
2018	13
2019	9
2020	7
2021	12
2022	16
2023	7
2024	0

1

Learning Neural Representations for Network Anomaly Detection.

Cao VL, Nicolau M, McDermott J. Cao VL, et al. IEEE Trans Cybern. 2019 Aug;49(8):3074-3087. doi: 10.1109/TCYB.2018.2838668. Epub 2018 Jun 15. IEEE Trans Cybern. 2019. PMID: 29994493

2

DOC-IDS: A Deep Learning-Based Method for Feature Extraction and Anomaly Detection in Network Traffic.

Yoshimura N, Kuzuno H, Shiraishi Y, Morii M. Yoshimura N, et al. Sensors (Basel). 2022 Jun 10;22(12):4405. doi: 10.3390/s22124405. Sensors (Basel). 2022. PMID: 35746191 Free PMC article.

3

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

Oluwasanmi A, Aftab MU, Baagyere E, Qin Z, Ahmad M, Mazzara M. Oluwasanmi A, et al. Sensors (Basel). 2021 Dec 24;22(1):123. doi: 10.3390/s22010123. Sensors (Basel). 2021. PMID: 35009666 Free PMC article.

4

Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation.

Ramirez Rivera A, Khan A, Bekkouch IEI, Sheikh TS. Ramirez Rivera A, et al. IEEE Trans Neural Netw Learn Syst. 2022 Jan;33(1):281-291. doi: 10.1109/TNNLS.2020.3027667. Epub 2022 Jan 5. IEEE Trans Neural Netw Learn Syst. 2022. PMID: 33064654

5

Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation.

Vaiyapuri T, Binbusayyis A. Vaiyapuri T, et al. PeerJ Comput Sci. 2020 Dec 7;6:e327. doi: 10.7717/peerj-cs.327. eCollection 2020. PeerJ Comput Sci. 2020. PMID: 33816977 Free PMC article.

6

Learning Latent Representation for IoT Anomaly Detection.

Vu L, Cao VL, Nguyen QU, Nguyen DN, Hoang DT, Dutkiewicz E. Vu L, et al. IEEE Trans Cybern. 2022 May;52(5):3769-3782. doi: 10.1109/TCYB.2020.3013416. Epub 2022 May 19. IEEE Trans Cybern. 2022. PMID: 32946404

7

Convolutional autoencoder based on latent subspace projection for anomaly detection.

Yu Q, Li C, Zhu Y, Kurita T. Yu Q, et al. Methods. 2023 Jun;214:48-59. doi: 10.1016/j.ymeth.2023.04.007. Epub 2023 Apr 28. Methods. 2023. PMID: 37120080

8

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network.

Yang Y, Zheng K, Wu C, Yang Y. Yang Y, et al. Sensors (Basel). 2019 Jun 2;19(11):2528. doi: 10.3390/s19112528. Sensors (Basel). 2019. PMID: 31159512 Free PMC article.

9

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection.

Li S, Lai S, Jiang Y, Wang W, Yi Y. Li S, et al. Comput Intell Neurosci. 2021 Nov 3;2021:4026132. doi: 10.1155/2021/4026132. eCollection 2021. Comput Intell Neurosci. 2021. PMID: 34777492 Free PMC article.

10

Learning feature representations with a cost-relevant sparse autoencoder.

Längkvist M, Loutfi A. Längkvist M, et al. Int J Neural Syst. 2015 Feb;25(1):1450034. doi: 10.1142/S0129065714500348. Int J Neural Syst. 2015. PMID: 25515941

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