Fetching the paper…
Reading the bibliography…
Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
C. Gentry, “Fully homomorphic encryption using ideal lattices,” in Proceedings of the forty-first annual ACM symposium on Theory of computing , 2009, pp. 169–178
2009
Earlier work this paper cites.
J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization.” Journal of machine learning research , vol. 13, no. 2, 2012
2012
Earlier work this paper cites.
A. Mantelero, “The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’,” Computer Law & Security Review , vol. 29, no. 3, pp. 229–235, 2013
2013
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations, ICLR 2015 , 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , 2016, pp. 785–794
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “Lightgbm: A highly efficient gradient boosting decision tree,” Advances in neural information processing systems , vol. 30, pp. 3146–3154, 2017
2017
Earlier work this paper cites.
Y. Xia, C. Liu, Y. Li, and N. Liu, “A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring,” Expert Systems with Applications , vol. 78, pp. 225–241, 2017
2017
Earlier work this paper cites.
L. Li, Y. Yu, S. Bai, J. Cheng, and X. Chen, “Towards effective network intrusion detection: A hybrid model integrating gini index and gbdt with pso,” Journal of Sensors , vol. 2018, 2018
2018
Earlier work this paper cites.
Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, “Kitsune: An ensemble of autoencoders for online network intrusion detection,” in Network and Distributed System Security Symposium, NDSS 2018
2018
Earlier work this paper cites.
I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “A detailed analysis of the cicids2017 data set,” in International Conference on Information Systems Security and Privacy . Springer, 2018, pp. 172–188
2018
Earlier work this paper cites.
S. Potluri, S. Ahmed, and C. Diedrich, “Convolutional neural networks for multi-class intrusion detection system,” in International Conference on Mining Intelligence and Knowledge Exploration . Springer, 2018, pp. 225–238
2018
Cited alongside, same era.
I. Sharafaldin, A. H. Lashkari, S. Hakak, and A. A. Ghorbani, “Developing realistic distributed denial of service (ddos) attack dataset and taxonomy,” in 2019 International Carnahan Conference on Security Technology (ICCST) . IEEE, 2019, pp. 1–8
2019
Cited alongside, same era.
T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A.-R. Sadeghi, “Dïot: A federated self-learning anomaly detection system for iot,” in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) . IEEE, 2019, pp. 756–767
2019
Cited alongside, same era.
H. Qiu, T. Dong, T. Zhang, J. Lu, G. Memmi, and M. Qiu, “Adversarial attacks against network intrusion detection in iot systems,” IEEE Internet of Things Journal , pp. 1–1, 2020
C. Xu, J. Shen, and X. Du, “A method of few-shot network intrusion detection based on meta-learning framework,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 3540–3552, 2020
2020
Later among the works it cites.
T. Bhavani, M. K. Rao, and A. M. Reddy, “Network intrusion detection system using random forest and decision tree machine learning techniques,” in 1st international conf. on sustainable technologies for computational intelligence . Springer, 2020, pp. 637–643
2020
Later among the works it cites.
2020
Later among the works it cites.
G. Xu, H. Li, Y. Zhang, S. Xu, J. Ning, and R. Deng, “Privacy-preserving federated deep learning with irregular users,” IEEE Transactions on Dependable and Secure Computing , pp. 1–1, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
M. Humayun, M. Niazi, N. Jhanjhi, M. Alshayeb, and S. Mahmood, “Cyber security threats and vulnerabilities: a systematic mapping study,” Arabian Journal for Science and Engineering , vol. 45, no. 4, pp. 3171–3189, 2020
2020
Cited alongside, same era.
M. A. Ferrag, L. Maglaras, A. Ahmim, M. Derdour, and H. Janicke, “Rdtids: Rules and decision tree-based intrusion detection system for internet-of-things networks,” Future internet , vol. 12, no. 3, p. 44, 2020
2020
Cited alongside, same era.
L. Zhu and S. Han, “Deep leakage from gradients,” in Federated Learning . Springer, 2020, pp. 17–31
2020
Cited alongside, same era.
M. MontazeriShatoori, L. Davidson, G. Kaur, and A. H. Lashkari, “Detection of doh tunnels using time-series classification of encrypted traffic,” in 2020 IEEE International Conference on Dependable, Autonomic and Secure Computing . IEEE, 2020, pp. 63–70
2020
Cited alongside, same era.
A. Habibi Lashkari, G. Kaur, and A. Rahali, “Didarknet: A contemporary approach to detect and characterize the darknet traffic using deep image learning,” in 2020 the 10th International Conference on Communication and Network Security , 2020, pp. 1–13
2020
Cited alongside, same era.
S. Mahdavifar, A. F. A. Kadir, R. Fatemi, D. Alhadidi, and A. A. Ghorbani, “Dynamic android malware category classification using semi-supervised deep learning,” in IEEE International Conference on Dependable, Autonomic and Secure Computing . IEEE, 2020, pp. 515–522
2020
Cited alongside, same era.
Z. Meng, M. Wang, J. Bai, M. Xu, H. Mao, and H. Hu, “Interpreting deep learning-based networking systems,” in Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication , 2020, pp. 154–171
2020
Cited alongside, same era.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” in Proceedings of Machine Learning and Systems 2020 , 2020
2020
Cited alongside, same era.
R. Hu, Y. Guo, H. Li, Q. Pei, and Y. Gong, “Personalized federated learning with differential privacy,” IEEE Internet of Things Journal , vol. 7, no. 10, pp. 9530–9539, 2020
2020
Later among the works it cites.
K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. Quek, and H. V. Poor, “Federated learning with differential privacy: Algorithms and performance analysis,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 3454–3469, 2020
2020
Later among the works it cites.
G. Qiu, X. Gui, and Y. Zhao, “Privacy-preserving linear regression on distributed data by homomorphic encryption and data masking,” IEEE Access , vol. 8, pp. 107 601–107 613, 2020
2020
Later among the works it cites.
M. Safaldin, M. Otair, and L. Abualigah, “Improved binary gray wolf optimizer and svm for intrusion detection system in wireless sensor networks,” Journal of ambient intelligence and humanized computing , vol. 12, no. 2, pp. 1559–1576, 2021
2021
Later among the works it cites.
X. Li, M. Zhu, L. T. Yang, M. Xu, Z. Ma, C. Zhong, H. Li, and Y. Xiang, “Sustainable ensemble learning driving intrusion detection model,” IEEE Transactions on Dependable and Secure Computing , pp. 1–1, 2021
2021
Later among the works it cites.
Q. Li, B. He, and D. Song, “Model-contrastive federated learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021
2021
Later among the works it cites.
W. Wen, C. Shang, Z. Dong, H.-C. Keh, and D. S. Roy, “An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks,” International Journal of Ad Hoc and Ubiquitous Computing , vol. 36, no. 1, pp. 20–31, 2021
2021
Later among the works it cites.
R. Panigrahi, S. Borah, A. K. Bhoi, M. F. Ijaz, M. Pramanik, Y. Kumar, and R. H. Jhaveri, “A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets,” Mathematics , vol. 9, no. 7, p. 751, 2021
2021
Later among the works it cites.
H. Yin, A. Mallya, A. Vahdat, J. M. Alvarez, J. Kautz, and P. Molchanov, “See through gradients: Image batch recovery via gradinversion,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021 , 2021
2021
Later among the works it cites.
W. Gao, S. Guo, T. Zhang, H. Qiu, Y. Wen, and Y. Liu, “Privacy-preserving collaborative learning with automatic transformation search,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021 , 2021
2021
Later among the works it cites.
H. Qiu, Q. Zheng, T. Zhang, M. Qiu, G. Memmi, and J. Lu, “Toward secure and efficient deep learning inference in dependable iot systems,” IEEE Internet of Things Journal , vol. 8, no. 5, pp. 3180–3188, 2021
2021
Later among the works it cites.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” 2021
2021
Later among the works it cites.