[1] Z. Liu, J. Yu, B. Yan, and G. Wang, “A deep 1-D CNN and bidirectional LSTM ensemble model with arbitration mechanism for LDDoS attack detection,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 6, no. 6, pp. 1396–1410, Dec. 2022.
[2] S. Bishnoi, S. Mohanty, and B. Sahoo, “A deep learning-based methodology in fog environment for DDoS attack detection,” in 2021 5th international conference on computing methodologies and communication (ICCMC), Apr. 2021, pp. 201–206.
[3] Á. L. Perales Gómez, L. F. Maimó, F. J. G. Clemente, J. A. M. Morales, A. H. Celdrán, and G. Bovet, “A methodology for evaluating the robustness of anomaly detectors to adversarial attacks in industrial scenarios,” IEEE Access Pract. Innov. Open Solut., vol. 10, pp. 124582–124594, 2022.
[4] M. I. Sayed, I. M. Sayem, S. Saha, and A. Haque, “A multi-classifier for DDoS attacks using stacking ensemble deep neural network,” in 2022 international wireless communications and mobile computing (IWCMC), May 2022, pp 1125–1130.
[5] A. Zainudin, L. A. C. Ahakonye, R. Akter, D.-S. Kim, and J.-M. Lee, “An efficient hybrid-DNN for DDoS detection and classification in software-defined IIoT networks,” IEEE Internet Things J., pp. 1–1, 2022.
[6] C. Yue, L. Wang, D. Wang, R. Duo, and X. Nie, “An ensemble intrusion detection method for train ethernet consist network based on CNN and RNN,” IEEE Access Pract. Innov. Open Solut., vol. 9, pp. 59527–59539, 2021.
[7] M. Roopak, G. Y. Tian, and J. Chambers, “An intrusion detection system against DDoS attacks in IoT networks,” in 2020 10th annual computing and communication workshop and conference (CCWC), Jan. 2020, pp. 0562–0567.
[8] J. Mao, M. Zhang, and Q. Xu, “CNN and LSTM based data-driven cyberattack detection for grid-connected PV inverter,” in 2022 IEEE 17th international conference on control & automation (ICCA), Jun. 2022, pp. 704–709.
[9] V. Gaur and R. Kumar, “DDoSLSTM: Detection of distributed denial of service attacks on IoT devices using LSTM model,” in 2022 international conference on communication, computing and internet of things (IC3IoT), Mar. 2022, pp. 01–07.
[10] Vaswani et al., “Attention is all you need,” in Advances in neural information processing systems, 2017, vol. 30.
[11] T. Lee, L. Chang, and C. Syu, “Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks,” in 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Jun. 2020, pp. 1–6.
[12] M. Roopak, G. Yun Tian, and J. Chambers, “Deep learning models for cyber security in IoT networks,” in 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), Jan. 2019, pp. 0452–0457.
[13] B. Nugraha and R. N. Murthy, “Deep learning-based slow DDoS attack detection in SDN-based networks,” in 2020 IEEE conference on network function virtualization and software defined networks (NFV-SDN), Nov. 2020, pp. 51–56.
[14] J. Spaulding and A. Mohaisen, “Defending internet of things against malicious domain names using D-FENS,” in 2018 IEEE/ACM symposium on edge computing (SEC), Oct. 2018, pp. 387–392.
[15] V. Kachavimath and Narayan D. G, “Distributed denial of service attacks detection using deep learning in software defined network,” in 2022 13th international conference on computing communication and networking technologies (ICCCNT), Oct. 2022, pp. 1–5.
[16] M. Ahsan, N. Rifat, M. Chowdhury, and R. Gomes, “Intrusion detection for IoT network security with deep neural network,” in 2022 IEEE international conference on electro information technology (eIT), May 2022, pp. 467–472.
[17] M. H. Haghighat and J. Li, “Intrusion detection system using voting-based neural network,” Tsinghua Sci. Technol., vol. 26, no. 4, pp. 484–495, Aug. 2021.
[18] N. Ruiz, B. Tavera, and A.-S. Abuzneid, “Intrusion detection system: The use of neural network packet classification,” in 2020 international conference on computational science and computational intelligence (CSCI), Dec. 2020, pp. 1276–1281.
[19] Hekmati, E. Grippo, and B. Krishnamachari, “Neural networks for DoS attack detection using an enhanced urban IoT dataset,” in 2022 international conference on computer communications and networks (ICCCN), Jul. 2022, pp. 1–8.
[20] M. Basnet, S. Poudyal, Mohd. H. Ali, and D. Dasgupta, “Ransomware detection using deep learning in the SCADA system of electric vehicle charging station,” in 2021 IEEE PES innovative smart grid technologies conference - latin america (ISGT latin america), Sep. 2021, pp. 1–5.
[21] M. Sarhan, S. Layeghy, N. Moustafa, and M. Portmann, "Netflow datasets for machine learning-based network intrusion detection systems," in Big Data Technologies and Applications: 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, December 11, 2020, Proceedings, vol. 10, pp. 117-135, Springer International Publishing, 2021.