Acceso abierto

A Recombination Generative Adversarial Network for Intrusion Detection


Cite

Andresini, G., Appice, A., De Rose, L. and Malerba, D. (2021). GAN augmentation to deal with imbalance in imaging-based intrusion detection, Future Generation Computer Systems 123(2021): 108–127, DOI:10.1016/j.future.2021.04.017. Search in Google Scholar

Bedi, P., Gupta, N. and Jindal, V. (2021). I-SIAMIDS: An improved SIAM-IDS for handling class imbalance in network-based intrusion detection systems, Applied Intelligence 51(2): 1133–1151. Search in Google Scholar

Brunner, C., Ko, A. and Fodor, S. (2022). An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection, Journal of Artificial Intelligence and Soft Computing Research 12(2): 149–163. Search in Google Scholar

Cui, J., Zong, L., Xie, J. and Tang, M. (2023). A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data, Applied Intelligence 53(1): 272–288. Search in Google Scholar

Dainotti, A., Pescape, A. and Claffy, K.C. (2012). Issues and future directions in traffic classification, IEEE Network 26(1,SI): 35–40. Search in Google Scholar

Fu, W., Qian, L. and Zhu, X. (2021). GAN-based intrusion detection data enhancement, Proceedings of the 33rd Chinese Control and Decision Conference (CCDC 2021), Kunming, China, pp. 2739–2744. Search in Google Scholar

Gelenbe, E. and Nakip, M. (2023). IoT network cybersecurity assessment with the associated random neural network, IEEE Access 11: 85501–85512, DOI: 10.1109/ACCESS.2023.3297977. Search in Google Scholar

Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2014). Generative adversarial nets, 28th Conference on Advances in Neural Information Processing Systems (NIPS 2014), Montreal, Canada, pp. 2672–2680. Search in Google Scholar

Gupta, N., Jindal, V. and Bedi, P. (2022). CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems, Computers & Security 112(2022): 102499, DOI:10.1016/j.cose.2021.102499. Search in Google Scholar

Jabbar, A., Li, X. and Omar, B. (2021). A survey on generative adversarial networks: Variants, applications, and training, ACM Computing Surveys 54(8): 1–49. Search in Google Scholar

Kanna, P.R. and Santhi, P. (2021). Unified deep learning approach for efficient intrusion detection system using integrated spatial-temporal features, Knowledge-Based Systems 226: 107132. Search in Google Scholar

Kumar, Y., Chouhan, L. and Subba, B. (2021). Deep learning techniques for anomaly based intrusion detection system: A survey, in S. Paul and J. Verma (Eds), 2021 International Conference on Computational Performance Evaluation (COMPE-2021), Shillong, India, pp. 915–920. Search in Google Scholar

Laghrissi, F., Douzi, S., Douzi, K. and Hssina, B. (2021). Intrusion detection systems using long short-term memory (LSTM), Journal of Big Data 8(1): 65. Search in Google Scholar

Liao, D., Zhou, R., Li, H., Zhang, M. and Chen, X. (2022). GE-IDS: An intrusion detection system based on grayscale and entropy, Peer-to-Peer Networking and Applications 15(3): 1521–1534. Search in Google Scholar

Liu, C., Antypenko, R., Sushko, I. and Zakharchenko, O. (2022). Intrusion detection system after data augmentation schemes based on the VAE and CVAE, IEEE Transactions on Reliability 71(2): 1000–1010. Search in Google Scholar

Nosouhian, S., Nosouhian, F. and Khoshouei, A.K. (2021). A review of recurrent neural network architecture for sequence learning: Comparison between LSTM and GRU, Preprints.org: 202107.0252, DOI: 10.20944/preprints202107.0252.v1. Search in Google Scholar

Oksuz, K., Cam, B.C., Kalkan, S. and Akbas, E. (2021). Imbalance problems in object detection: A review, IEEE Transactions on Pattern Analysis and Machine Intelligence 43(10): 3388–3415. Search in Google Scholar

Qazi, E.U.H., Faheem, M.H. and Zia, T. (2023). HDLNIDS: Hybrid deep-learning-based network intrusion detection system, Applied Sciences 13(8): 4921. Search in Google Scholar

Radford, A., Metz, L. and Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks, ArXiv: 1511.06434. Search in Google Scholar

Sabahi, F. and Movaghar, A. (2008). Intrusion detection: A survey, 2008 3rd International Conference on Systems and Networks Communications, Slema,Malta, pp. 23–26, DOI: 10.1109/ICSNC.2008.44. Search in Google Scholar

Sun, H., Wan, L., Liu, M. and Wang, B. (2023). Few-shot network intrusion detection based on prototypical capsule network with attention mechanism, Plos ONE 18(4): e0284632. Search in Google Scholar

Thakkar, A. and Lohiya, R. (2023). Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system, Information Fusion 90(2023): 353–363, DOI: 10.1016/j.inffus.2022.09.026. Search in Google Scholar

Wang, W., Sheng, Y., wang, J., Zeng, X., Ye, X., Huang, Y. and Zhu, M. (2018). HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection, IEEE Access 6(2018): 1792–1806, DOI: 10.1109/ACCESS.2017.2780250. Search in Google Scholar

Wang, Z., Liu, Y., He, D. and Chan, S. (2021). Intrusion detection methods based on integrated deep learning model, Computers & Security 103(2021): 102177. Search in Google Scholar

Xiao, Y., Xing, C., Zhang, T. and Zhao, Z. (2019). An intrusion detection model based on feature reduction and convolutional neural networks, IEEE Access 7: 42210–42219, DOI: 10.1109/ACCESS.2019.2904620. Search in Google Scholar

Yuan, L., Yu, S., Yang, Z., Duan, M. and Li, K. (2023). A data balancing approach based on generative adversarial network, Future Generation Computer Systems 141(2023): 768–776. Search in Google Scholar

Zhang, H., Ge, L. and Wang, Z. (2022a). A high performance intrusion detection system using LightGBM based on oversampling and undersampling, in D. Huang et al. (Eds), Intelligent Computing Theories and Application (ICIC 2022), Lecture Notes in Computer Science, Vol. 13393, Springer, Cham, pp. 638–652, DOI: 10.1007/978-3-031-13870-6 53. Search in Google Scholar

Zhang, X., Wang, J. and Zhu, S. (2022b). Dual generative adversarial networks based unknown encryption ransomware attack detection, IEEE Access 10(2021): 900–913, DOI: 10.1109/ACCESS.2021.3128024. Search in Google Scholar

Zhou, Y., Cheng, G., Jiang, S. and Dai, M. (2020). Building an efficient intrusion detection system based on feature selection and ensemble classifier, Computer Networks 174(2020): 107247, DOI: 10.1016/j.comnet.2020.107247. Search in Google Scholar

Zou, L., Luo, X., Zhang, Y., Yang, X. and Wang, X. (2023). HC-DTTSVM: A network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering, IEEE Access 11(2023): 21404–21416, DOI: 10.1109/ACCESS.2023.3251354. Search in Google Scholar

eISSN:
2083-8492
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Mathematics, Applied Mathematics