Fetching the paper…
Reading the bibliography…
The Internet has become a prime subject to security attacks and intrusions by attackers.
W. G. Cochran, Sampling Techniques, 3rd Edition . John Wiley, 1977
1977
Earlier work this paper cites.
B. Mukherjee, L. Heberlein, and K. Levitt, “Network intrusion detection,” IEEE Network , vol. 8, no. 3, pp. 26–41, 1994
1994
Earlier work this paper cites.
J. Sola and J. Sevilla, “Importance of input data normalization for the application of neural networks to complex industrial problems,” vol. 44, pp. 1464–1468, 1997
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
G. Vigna, W. K. Robertson, and D. Balzarotti, “Testing network-based intrusion detection signatures using mutant exploits,” in Proceedings of the 11th ACM Conference on Computer and Communications Security, CCS 2004, Washington, DC, USA, October 25-29, 2004 . ACM, 2004, pp. 21–30
2004
Earlier work this paper cites.
S. Yang and A. Browne, “Neural network ensembles: combining multiple models for enhanced performance using a multistage approach,” Expert Syst. J. Knowl. Eng. , vol. 21, no. 5, pp. 279–288, 2004
2004
Earlier work this paper cites.
P. Garcia-Teodoro, J. E. D. Verdejo, G. Maciá-Fernández, and E. Vázquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Comput. Secur. , vol. 28, no. 1-2, pp. 18–28, 2009
2009
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel , J. Fürnkranz and T. Joachims, Eds. Omnipress, 2010, pp. 807–814. [Online]. Available: https://icml.cc/Conferences/2010/papers/432.pdf
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
Y. Bengio, “Deep learning of representations for unsupervised and transfer learning,” in Proceedings of ICML Workshop on Unsupervised and Transfer Learning , ser. Proceedings of Machine Learning Research, I. Guyon, G. Dror, V. Lemaire, G. Taylor, and D. Silver, Eds., vol. 27. Bellevue, Washington, USA: PMLR, 02 Jul 2012, pp. 17–36. [Online]. Available: http://proceedings.mlr.press/v27/bengio12a.html
2012
Earlier work this paper cites.
H. Liao, C. R. Lin, Y. Lin, and K. Tung, “Intrusion detection system: A comprehensive review,” J. Netw. Comput. Appl. , vol. 36, no. 1, pp. 16–24, 2013
2013
Earlier work this paper cites.
B. Claise, B. Trammell, and P. Aitken, “Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of Flow Information,” RFC , vol. 7011, pp. 1–76, 2013
2013
Earlier work this paper cites.
A. D. Khairkar, D. D. Kshirsagar, and S. Kumar, “Ontology for detection of web attacks,” in 2013 International Conference on Communication Systems and Network Technologies , 2013, pp. 612–615
2013
Cited alongside, same era.
M. Lin, Q. Chen, and S. Yan, “Network in network,” arXiv, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
F. Chollet et al. , “Keras,” https://keras.io
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016, http://www.deeplearningbook.org
2016
Cited alongside, same era.
R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep learning approach for intelligent intrusion detection system,” IEEE Access , vol. 7, pp. 41 525–41 550, 2019
2019
Later among the works it cites.
R. K. Malaiya, D. Kwon, S. C. Suh, H. Kim, I. Kim, and J. Kim, “An empirical evaluation of deep learning for network anomaly detection,” IEEE Access , vol. 7, pp. 140 806–140 817, 2019
2019
Later among the works it cites.
Y. Zhang, X. Chen, D. Guo, M. Song, Y. Teng, and X. Wang, “PCCN: parallel cross convolutional neural network for abnormal network traffic flows detection in multi-class imbalanced network traffic flows,” IEEE Access , vol. 7, pp. 119 904–119 916, 2019
2019
Later among the works it cites.
X. Zhang, J. Chen, Y. Zhou, L. Han, and J. Lin, “A multiple-layer representation learning model for network-based attack detection,” IEEE Access , vol. 7, pp. 91 992–92 008, 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Lu, H. Pu, F. Wang, Z. Hu, and L. Wang, “The expressive power of neural networks: A view from the width,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , 2017, pp. 6231–6239. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/32cbf687880eb1674a07bf717761dd3a-Abstract.html
2017
Cited alongside, same era.
M. F. Umer, M. Sher, and Y. Bi, “Flow-based intrusion detection: Techniques and challenges,” Comput. Secur. , vol. 70, pp. 238–254, 2017
2017
Cited alongside, same era.
I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proceedings of the 4th International Conference on Information Systems Security and Privacy, ICISSP 2018, Funchal, Madeira - Portugal, January 22-24, 2018 , P. Mori, S. Furnell, and O. Camp, Eds. SciTePress, 2018, pp. 108–116
2018
Cited alongside, same era.
N. Marir, H. Wang, G. Feng, B. Li, and M. Jia, “Distributed abnormal behavior detection approach based on deep belief network and ensemble SVM using spark,” IEEE Access , vol. 6, pp. 59 657–59 671, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Li, Y. Qu, F. Chao, H. P. H. Shum, E. S. L. Ho, and L. Yang, Machine Learning Algorithms for Network Intrusion Detection . Cham: Springer International Publishing, 2019, pp. 151–179. [Online]. Available: https://doi.org/10.1007/978-3-319-98842-9_6
2019
Cited alongside, same era.
Y. Zhang, X. Chen, L. Jin, X. Wang, and D. Guo, “Network intrusion detection: Based on deep hierarchical network and original flow data,” IEEE Access , vol. 7, pp. 37 004–37 016, 2019
2019
Cited alongside, same era.
M. F. López-Vizcaíno, F. J. Nóvoa, D. Fernández, V. Carneiro, and F. Cacheda, “Early intrusion detection for OS scan attacks,” in 18th IEEE International Symposium on Network Computing and Applications, NCA 2019, Cambridge, MA, USA, September 26-28, 2019 , A. Gkoulalas-Divanis, M. Marchetti, and D. R. Avresky, Eds. IEEE, 2019, pp. 1–5
2019
Later among the works it cites.
Q. Zhu, “On the performance of matthews correlation coefficient (MCC) for imbalanced dataset,” Pattern Recognit. Lett. , vol. 136, pp. 71–80, 2020
2020
Later among the works it cites.
G. Andresini, A. Appice, N. D. Mauro, C. Loglisci, and D. Malerba, “Multi-channel deep feature learning for intrusion detection,” IEEE Access , vol. 8, pp. 53 346–53 359, 2020
2020
Later among the works it cites.
Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Transactions on Emerging Telecommunications Technologies , vol. 32, no. 1, p. e4150, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.4150
2021
Later among the works it cites.
Google Cloud, “Minimizing real-time prediction serving latency in machine learning,” online; accessed on 26 December, 2021. [Online]. Available: https://cloud.google.com/architecture/minimizing-predictive-serving-latency-in-machine-learning
2021
Later among the works it cites.
J. Gu and S. Lu, “An effective intrusion detection approach using SVM with naïve bayes feature embedding,” Comput. Secur. , vol. 103, pp. 102–158, 2021
2021
Later among the works it cites.
S. Rajagopal, P. P. Kundapur, and H. K. S., “Towards effective network intrusion detection: From concept to creation on azure cloud,” IEEE Access , vol. 9, pp. 19 723–19 742, 2021
2021
Later among the works it cites.
Y. Zhu, D. Han, and X. Yin, “A hierarchical network intrusion detection model based on unsupervised clustering,” in MEDES ’21: Proceedings of the 13th International Conference on Management of Digital EcoSystems, Virtual Event, Tunisia, November 1 - 3, 2021 , R. Chbeir, Y. Manolopoulos, L. Bellatreche, D. Benslimane, M. Ivanovic, and Z. Maamar, Eds. ACM, 2021, pp. 22–29
2021
Later among the works it cites.