{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T12:36:09Z","timestamp":1776947769656,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>DDoS (Distributed Denial of Service) has emerged as a serious and challenging threat to computer networks and information systems\u2019 security and integrity. Before any remedial measures can be implemented, DDoS assaults must first be detected. DDoS attacks can be identified and characterized with satisfactory achievement employing ML (Machine Learning) and DL (Deep Learning). However, new varieties of aggression arise as the technology for DDoS attacks keep evolving. This research explores the impact of a new incarnation of DDoS attack\u2013adversarial DDoS attack. There are established works on ML-based DDoS detection and GAN (Generative Adversarial Network) based adversarial DDoS synthesis. We confirm these findings in our experiments. Experiments in this study involve the extension and application of the GAN, a machine learning framework with symmetric form having two contending neural networks. We synthesize adversarial DDoS attacks utilizing Wasserstein Generative Adversarial Networks featuring Gradient Penalty (GP-WGAN). Experiment results indicate that the synthesized traffic can traverse the detection systems such as k-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Random Forest (RF) without being identified. This observation is a sobering and pessimistic wake-up call, implying that countermeasures to adversarial DDoS attacks are urgently needed. To this problem, we propose a novel DDoS detection framework featuring GAN with Dual Discriminators (GANDD). The additional discriminator is designed to identify adversary DDoS traffic. The proposed GANDD can be an effective solution to adversarial DDoS attacks, as evidenced by the experimental results. We use adversarial DDoS traffic synthesized by GP-WGAN to train GANDD and validate it alongside three other DL technologies: DNN (Deep Neural Network), LSTM (Long Short-Term Memory) and GAN. GANDD outperformed the other DL models, demonstrating its protection with a TPR of 84.3%. A more sophisticated test was also conducted to examine GANDD\u2019s ability to handle unseen adversarial attacks. GANDD was evaluated with adversarial traffic not generated from its training data. GANDD still proved effective with a TPR around 71.3% compared to 7.4% of LSTM.<\/jats:p>","DOI":"10.3390\/sym14010066","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:35:09Z","timestamp":1641771309000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Detection of Adversarial DDoS Attacks Using Generative Adversarial Networks with Dual Discriminators"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3187-458X","authenticated-orcid":false,"given":"Chin-Shiuh","family":"Shieh","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6835-1841","authenticated-orcid":false,"given":"Thanh-Tuan","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan"},{"name":"Department of Electronic and Automation Engineering, Nha Trang University, Nha Trang 650000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4567-3925","authenticated-orcid":false,"given":"Wan-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan"}]},{"given":"Yong-Lin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4576-3787","authenticated-orcid":false,"given":"Mong-Fong","family":"Horng","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3112-3594","authenticated-orcid":false,"given":"Tsair-Fwu","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan"}]},{"given":"Denis","family":"Miu","sequence":"additional","affiliation":[{"name":"Genie Networks Ltd., Taipei 11444, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","unstructured":"(2021, October 12). Genie-Networks, DDoS Attack Statistics and Trends Report for 2020 and 2021. Available online: https:\/\/www.genie-networks.com\/gnnews\/ddos-attack-statistics-and-trends-report-for-h1-2020."},{"key":"ref_2","first-page":"187","article-title":"A Survey on mitigation techniques against DDoS attacks on cloud computing architecture","volume":"28","author":"Bakr","year":"2019","journal-title":"J. Adv. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Priya, S.S., Sivaram, M., Yuvaraj, D., and Jayanthiladevi, A. (2020, January 12\u201314). Machine learning based DDoS detection. Proceedings of the 2020 International Conference on Emerging Smart Computing and Informatics, Pune, India.","DOI":"10.1109\/ESCI48226.2020.9167642"},{"key":"ref_4","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014, January 14\u201316). Intriguing properties of neural networks. Proceedings of the 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"143573","DOI":"10.1109\/ACCESS.2019.2945787","article-title":"Review: Build a roadmap for stepping into the field of anti-malware research smoothly","volume":"7","author":"Han","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4907754","DOI":"10.1155\/2021\/4907754","article-title":"A Survey on Adversarial Attack in the Age of Artificial Intelligence","volume":"2021","author":"Kong","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_7","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial networks. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_8","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_9","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017, January 4\u20139). Improved training of Wasserstein GANs. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_10","unstructured":"Nguyen, T.D., Le, T., Vu, H., and Phung, D. (2017, January 4\u20139). Dual Discriminator Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems 30, Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhao, Y., and Zhang, H. (2020, January 27\u201329). Dual-discriminator GAN: A GAN way of profile face recognition. Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA50127.2020.9182406"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cheng, J., Yin, J., Liu, Y., Cai, Z., and Wu, C. (2009, January 4\u20136). DDoS attack detection using IP address feature interaction. Proceedings of the IEEE International Conference on Intelligent Networking and Collaborative Systems, Barcelona, Spain.","DOI":"10.1109\/INCOS.2009.34"},{"key":"ref_13","unstructured":"Vu, N.H. (2008, January 16\u201318). DDoS attack detection using K-Nearest Neighbor classifier method. Proceedings of the International Conference on Telehealth\/Assistive Technologies, Baltimore, MD, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140","DOI":"10.11591\/eei.v6i2.605","article-title":"Review of detection DDoS attack detection using naive bayes classifier for network forensics","volume":"6","author":"Fadlil","year":"2017","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, C., Zheng, J., and Li, X. (2017, January 9\u201310). Research on DDoS attacks detection based on RDF-SVM. Proceedings of the 10th International Conference on Intelligent Computation Technology and Automation, Changsha, China.","DOI":"10.1109\/ICICTA.2017.43"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dincalp, U. (2018, January 19\u201321). Anomaly based distributed denial of service attack detection and prevention with machine learning. Proceedings of the 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, Ankara, Turkey.","DOI":"10.1109\/ISMSIT.2018.8567252"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ahanger, T.A. (2017, January 22\u201324). An effective approach of detecting DDoS using artificial neural networks. Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, Chennai, India.","DOI":"10.1109\/WiSPNET.2017.8299853"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, Y., and Lu, Y. (2019, January 21\u201322). LSTM-BA: DDoS detection approach combining LSTM and Bayes. Proceedings of the 7th International Conference on Advanced Cloud and Big Data, Suzhou, China.","DOI":"10.1109\/CBD.2019.00041"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhang, J., Xu, Y., and Chao, J. (2020, January 20\u201324). DDoS attack detection with AutoEncoder. Proceedings of the IEEE\/IFIP Network Operations and Management Symposium, Budapest, Hungary.","DOI":"10.1109\/NOMS47738.2020.9110372"},{"key":"ref_20","unstructured":"Li, Z., Sun, C., Liu, C., Chen, X., Wang, M., and Liu, Y. (2020). RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, D. (2017, January 6\u20138). A new mimicking attack by LSGAN. Proceedings of the 2017 IEEE 29th International Conference on Tools with Artificial Intelligence, Boston, MA, USA.","DOI":"10.1109\/ICTAI.2017.00074"},{"key":"ref_22","unstructured":"Hu, W., and Tan, Y. (2017). Generating adversarial malware examples for black-box attacks based on GAN. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kolosnjaji, B., Demontis, A., Biggio, B., Maiorca, D., Giacinto, G., Eckert, C., and Roli, F. (2018, January 3\u20137). Adversarial malware binaries: Evading deep learning for malware detection in executables. Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy.","DOI":"10.23919\/EUSIPCO.2018.8553214"},{"key":"ref_24","unstructured":"Song, W., Li, X., Afroz, S., Garg, D., Kuznetsov, D., and Yin, H. (2021). Automatic generation of adversarial examples for interpreting malware classifiers. arXiv."},{"key":"ref_25","unstructured":"Ebrahimi, M., Zhang, N., Hu, J., Raza, M.T., and Chen, H. (2020). Binary black-box evasion attacks against deep learning based static malware detectors with adversarial byte-level language model. arXiv."},{"key":"ref_26","unstructured":"IBM (2021, October 12). Security: Adversarial Detection Module. CYBERSEC, Available online: https:\/\/www.ithome.com.tw\/news\/139848."},{"key":"ref_27","unstructured":"Canadian Institute for Cybersecurity (2021, October 12). NSL-KDD. Available online: https:\/\/www.unb.ca\/cic\/datasets\/nsl.html."},{"key":"ref_28","unstructured":"Canadian Institute for Cybersecurity (2021, October 12). IDS-2017. Available online: https:\/\/www.unb.ca\/cic\/datasets\/ids-2017.html."},{"key":"ref_29","unstructured":"(2021, October 12). University of California, Irvine, KDD Cup 1999 Data. Available online: http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html."},{"key":"ref_30","unstructured":"Canadian Institute for Cybersecurity (2021, October 12). CICFlowMeter. Available online: https:\/\/github.com\/CanadianInstituteForCybersecurity\/CICFlowMeter."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/1\/66\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:00:03Z","timestamp":1760364003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/1\/66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,4]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["sym14010066"],"URL":"https:\/\/doi.org\/10.3390\/sym14010066","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,4]]}}}