Fetching the paper…
Reading the bibliography…
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs.
1902
Earlier work this paper cites.
J. Ryan, M.-J. Lin, and R. Miikkulainen, “Intrusion detection with neural networks,” in Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems 10 , ser. NIPS ’97, 1998, pp. 943–949
1998
Earlier work this paper cites.
R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning,” Artificial intelligence review , vol. 18, no. 2, pp. 77–95, 2002
2002
Earlier work this paper cites.
S. Marsland, “Novelty detection in learning systems,” Neural Comp. Surveys , 2003
2003
Earlier work this paper cites.
V. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial intelligence review , vol. 22, no. 2, pp. 85–126, 2004
2004
Earlier work this paper cites.
A. Patcha and J.-M. Park, “An overview of anomaly detection techniques: Existing solutions and latest technological trends,” Computer networks , vol. 51, no. 12, pp. 3448–3470, 2007
2007
Earlier work this paper cites.
J. Elson, J. J. Douceur, J. Howell, and J. Saul, “Asirra: a captcha that exploits interest-aligned manual image categorization,” 2007
2007
Earlier work this paper cites.
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. , vol. 41, no. 3, pp. 15:1–15:58, Jul. 2009
2009
Earlier work this paper cites.
A. Krizhevsky et al. , “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
Y. LeCun and C. Cortes, “MNIST handwritten digit database,” 2010
2010
Earlier work this paper cites.
A. Coates, A. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 215–223
2011
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011
2011
Earlier work this paper cites.
A. Shilton, S. Rajasegarar, and M. Palaniswami, “Combined multiclass classification and anomaly detection for large-scale wireless sensor networks,” in 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing , April 2013, pp. 491–496
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
K. Muandet, D. Balduzzi, and B. Schölkopf, “Domain generalization via invariant feature representation,” in International Conference on Machine Learning , 2013, pp. 10–18
2013
Earlier work this paper cites.
M. A. F. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, “Review: A review of novelty detection,” Signal Process. , vol. 99, pp. 215–249, Jun. 2014
2014
Earlier work this paper cites.
L. Kalinichenko, I. Shanin, and I. Taraban, “Methods for anomaly detection: A survey,” in CEUR Workshop Proceedings , 2014
2014
Earlier work this paper cites.
T. Dunning and E. Friedman, Practical Machine Learning: A New Look at Anomaly Detection . O’Reilly Media, 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
N. Gao, L. Gao, Q. Gao, and H. Wang, “An intrusion detection model based on deep belief networks,” in 2014 Second International Conference on Advanced Cloud and Big Data , Nov 2014, pp. 247–252
2014
Earlier work this paper cites.
S. Agrawal and J. Agrawal, “Survey on anomaly detection using data mining techniques,” Procedia Computer Science , vol. 60, pp. 708–713, 2015
2015
Earlier work this paper cites.
J. M. Hernández-Lobato and R. Adams, “Probabilistic backpropagation for scalable learning of bayesian neural networks,” in International Conference on Machine Learning , 2015, pp. 1861–1869
2015
Earlier work this paper cites.
C. Aggarwal, Outlier Analysis . Springer International Publishing, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” arXiv preprint arXiv:1605.07146 , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
W. Hardy, L. Chen, S. Hou, Y. Ye, and X. Li, “Dl4md: A deep learning framework for intelligent malware detection,” in Proceedings of the International Conference on Data Mining (DMIN) . The Steering Committee of The World Congress in Computer Science, Computer, 2016, p. 61
2016
Earlier work this paper cites.
K. Mehrotra, C. Mohan, and H. Huang, Anomaly Detection Principles and Algorithms . Springer International Publishing, 2017
2017
Earlier work this paper cites.
M. Bhuyan, D. Bhattacharyya, and J. Kalita, Network Traffic Anomaly Detection and Prevention: Concepts, Techniques, and Tools . Springer International Publishing, 2017
2017
Earlier work this paper cites.
W. Lawson, E. Bekele, and K. Sullivan, “Finding anomalies with generative adversarial networks for a patrolbot,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , July 2017, pp. 484–485
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708
2017
Earlier work this paper cites.
N. Ruchansky, S. Seo, and Y. Liu, “Csi: A hybrid deep model for fake news detection,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 2017, pp. 797–806
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Miller, Y. Wang, and G. Kesidis, “When not to classify: Anomaly detection of attacks (ADA) on DNN classifiers at test time,” Neural Computation , 12 2017
2017
Earlier work this paper cites.
F. Carrara, F. Falchi, R. Caldelli, G. Amato, R. Fumarola, and R. Becarelli, “Detecting adversarial example attacks to deep neural networks,” in Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing . ACM, 2017, p. 38
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Lu, T. Issaranon, and D. A. Forsyth, “Safetynet: Detecting and rejecting adversarial examples robustly,” 2017 IEEE International Conference on Computer Vision (ICCV) , pp. 446–454, 2017
2017
Cited alongside, same era.
Y. Yu, J. Long, and Z. Cai, “Network intrusion detection through stacking dilated convolutional autoencoders,” Security and Communication Networks , vol. 2017, 2017
2017
Cited alongside, same era.
T. S. Buda, B. Caglayan, and H. Assem, “Deepad: A generic framework based on deep learning for time series anomaly detection,” in Advances in Knowledge Discovery and Data Mining , D. Phung, V. S. Tseng, G. I. Webb, B. Ho, M. Ganji, and L. Rashidi, Eds., 2018, pp. 577–588
2018
Later among the works it cites.
Y. Yuan, G. Xun, F. Ma, Y. Wang, N. Du, K. Jia, L. Su, and A. Zhang, “Muvan: A multi-view attention network for multivariate temporal data,” in 2018 IEEE International Conference on Data Mining (ICDM) , Nov 2018, pp. 717–726
2018
Later among the works it cites.
2018
Later among the works it cites.
H. Zhang, K. Nian, T. F. Coleman, and Y. Li, “Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection,” International Journal of Data Science and Analytics , pp. 1–19, 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. C. Aygun and A. G. Yavuz, “Network anomaly detection with stochastically improved autoencoder based models,” in 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) , June 2017, pp. 193–198
2017
Cited alongside, same era.
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel, “Adversarial examples for malware detection,” in European Symposium on Research in Computer Security . Springer, 2017, pp. 62–79
2017
Cited alongside, same era.
M. Gutoski, N. M. R. Aquino, M. Ribeiro, A. Lazzaretti, and H. S. Lopes, “Detection of video anomalies using convolutional autoencoders and one-class support vector machines,” 2017
2017
Cited alongside, same era.
H. Lakkaraju, E. Kamar, R. Caruana, and E. Horvitz, “Identifying unknown unknowns in the open world: Representations and policies for guided exploration,” in Thirty-First AAAI Conference on Artificial Intelligence , 2017
2017
Cited alongside, same era.
N. Carlini and D. Wagner, “Adversarial examples are not easily detected: Bypassing ten detection methods,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security , 2017, pp. 3–14
2017
Cited alongside, same era.
M. Salehi and L. Rashidi, “A survey on anomaly detection in evolving data: [with application to forest fire risk prediction],” SIGKDD Explor. Newsl. , vol. 20, no. 1, pp. 13–23, May 2018
2018
Cited alongside, same era.
G. Muruti, F. A. Rahim, and Z. bin Ibrahim, “A survey on anomalies detection techniques and measurement methods,” in 2018 IEEE Conference on Application, Information and Network Security (AINS) , Nov 2018, pp. 81–86
2018
Cited alongside, same era.
M.-R. Bouguelia, S. Nowaczyk, and A. H. Payberah, “An adaptive algorithm for anomaly and novelty detection in evolving data streams,” Data mining and knowledge discovery , vol. 32, no. 6, pp. 1597–1633, 2018
2018
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
C. Xiao, R. Deng, B. Li, F. Yu, M. Liu, and D. Song, “Characterizing adversarial examples based on spatial consistency information for semantic segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 217–234
2018
Later among the works it cites.
2018
Later among the works it cites.
L. Zhang, “Transfer adaptation learning: A decade survey,” arXiv preprint arXiv:1903.04687 , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Q. Yu and K. Aizawa, “Unsupervised out-of-distribution detection by maximum classifier discrepancy,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9518–9526
2019
Later among the works it cites.
P. Schulam and S. Saria, “Can you trust this prediction? auditing pointwise reliability after learning,” in Proceedings of Machine Learning Research , ser. Proceedings of Machine Learning Research, vol. 89. PMLR, 16–18 Apr 2019, pp. 1022–1031
2019
Later among the works it cites.
2019
Later among the works it cites.
W. Wang, M. Zhao, and J. Wang, “Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network,” Journal of Ambient Intelligence and Humanized Computing , vol. 10, no. 8, pp. 3035–3043, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Snoek, Y. Ovadia, E. Fertig, B. Lakshminarayanan, S. Nowozin, D. Sculley, J. Dillon, J. Ren, and Z. Nado, “Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift,” in Advances in Neural Information Processing Systems , 2019, pp. 13 969–13 980
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Ma, Y. Liu, G. Tao, W.-C. Lee, and X. Zhang, “Nic: Detecting adversarial samples with neural network invariant checking,” in NDSS , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Dubey, L. v. d. Maaten, Z. Yalniz, Y. Li, and D. Mahajan, “Defense against adversarial images using web-scale nearest-neighbor search,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8767–8776
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Monteiro, I. Albuquerque, Z. Akhtar, and T. H. Falk, “Generalizable adversarial examples detection based on bi-model decision mismatch,” in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) . IEEE, 2019, pp. 2839–2844
2019
Later among the works it cites.
2019
Later among the works it cites.
Q. Guo, Z. Li, B. An, P. Hui, J. Huang, L. Zhang, and M. Zhao, “Securing the deep fraud detector in large-scale e-commerce platform via adversarial machine learning approach,” in The World Wide Web Conference . ACM, 2019, pp. 616–626
2019
Later among the works it cites.
A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nature medicine , vol. 25, no. 1, pp. 24–29, 2019
2019
Later among the works it cites.
O. Suciu, S. E. Coull, and J. Johns, “Exploring adversarial examples in malware detection,” in 2019 IEEE Security and Privacy Workshops (SPW) . IEEE, 2019, pp. 8–14
2019
Later among the works it cites.
G. Tripathi, K. Singh, and D. K. Vishwakarma, “Convolutional neural networks for crowd behaviour analysis: a survey,” The Visual Computer , vol. 35, no. 5, pp. 753–776, May 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.