|
[1] IDC. (2022, 7月27日). 新混合型辦公模式加速台灣公有雲服務市場高速成長，2022至2026年台灣公有雲市場年複合成長率25.2%。International Data Corporation。https://www.idc.com/getdoc.jsp?containerId=prAP49540022
[2] Amazon Web Services, Inc. (無日期). 立即開始在 AWS 進行建置。取自 https://aws.amazon.com/tw/
[3] Microsoft Corporation. (2010). Microsoft Azure 首頁。取自 https://azure.microsoft.com/
[4] Dahan, A. (2021, 10月11日). Business as usual for Azure customers despite 2.4 Tbps DDoS attack. Microsoft Azure Blog. 取自 https://azure.microsoft.com/en-us/blog/business-as-usual-for-azure-customers-despite-24-tbps-ddos-attack/
[5] Kiner, E., & Konduru, S. (2022, 8月19日). How Google Cloud blocked the largest Layer 7 DDoS attack at 46 million rps. Google Cloud Blog. 取自 https://cloud.google.com/blog/products/identity-security/how-google-cloud-blocked-largest-layer-7-ddos-attack-at-46-million-rps
[6] Geenens, P.，& Smith, D.（編輯）。 （2022）。 2022 H1 全球威脅分析報告。 Radware。 取自 H1-2022-Threat-Analysis-Report_2022_Report-V2.pdf
[7] 溫貴香. (2022, 8月2日). 美國議長裴洛西訪台前總統府等網站遇襲 專家：中國駭客所為。中央社即時新聞。取自 https://www.cna.com.tw/news/aipl/202208025009.aspx
蘇龍麒. (編輯). (2022, 8月2日). 美國議長裴洛西訪台前總統府等網站遇襲 專家：中國駭客所為。中央社即時新聞。取自 https://www.cna.com.tw/news/aipl/202208025009.aspx
[8] 陳俐穎. (2022, 8月4日). 超過前2月總和！裴洛西訪台 台電遭資安攻擊達490萬次。ETtoday財經雲。取自 https://finance.ettoday.net/news/2309280
[9] 新聞傳播處. (107, 5月12日). 資安產業發展行動計畫。取自行政院全球資訊網 https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/4415fe14-477e-45cb-9a09-cb6962054fa6
[10] Google Cloud. (無日期). Google Cloud Armor。取自 https://cloud.google.com/armor/
[11] Amazon Web Services, Inc. (無日期). AWS WAF。取自 https://aws.amazon.com/tw/shield/
[12] Liang, X., & Znati, T. (2019). An Empirical Study of Intelligent Approaches to DDoS Detection in Large Scale Networks. 2019 International Conference on Computing, Networking and Communications (ICNC), 2325–2626, 821–827. https://ieeexplore.ieee.org/document/8685519
[13] Sanchez, O. R., Repetto, M., Carrega, A., Bolla, R., & Pajo, J. F. (2021). Feature Selection Evaluation towards a Lightweight Deep Learning DDoS Detector. ICC 2021 - IEEE International Conference on Communications, 1938–1883, 1–6. https://ieeexplore.ieee.org/document/9500458
[14] Xu, Y., Sun, H., Xiang, F., & Sun, Z. (2019). Efficient DDoS Detection Based on K-FKNN in Software Defined Network. IEEE Access, 7(2169–3536), 160536–160545. https://ieeexplore.ieee.org/document/8889743
[15] Tsobdjou, L. D., Pierre, S., & Quintero, A. (2022). An Online Entropy-Based DDoS Flooding Attack Detection System With Dynamic Threshold. IEEE Transactions on Network and Service Management, 19(1932–4537), 1679–1689. https://ieeexplore.ieee.org/document/9678955
[16] Mishra, A., Gupta, B. B., Peraković, D., Peñalvo, F. J. garcía, & Hsu, C. (2021). Classification Based Machine Learning for Detection of DDoS Attack in Cloud Computing. 2021 IEEE International Conference on Consumer Electronics (ICCE), 2158–4001, 1–4. https://ieeexplore.ieee.org/document/9427665
[17] Ma, L., Chai, Y., Cui, L., Ma, D., Fu, Y., & Xiao, A. (2020). A Deep Learning-Based DDoS Detection Framework for Internet of Things. ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 1938–1883, 1–6. https://ieeexplore.ieee.org/document/9148944
[18] Gaurav, A., Gupta, B. B., Hsu, C., Peraković, D., & Peñalvo, F. J. garcía. (2021). Filtering of Distributed Denial of Services (DDoS) Attacks in Cloud Computing Environment. 2021 IEEE International Conference on Communications Workshops (ICC Workshops), 2694–2941, 1–6. https://ieeexplore.ieee.org/document/9473886
[19] Shi, Z., Li, J., & Wu, C. (2019). DeepDDoS: Online DDoS Attack Detection. 2019 IEEE Global Communications Conference (GLOBECOM), 2576–6813, 1–6. https://ieeexplore.ieee.org/document/9013186
[20] Lin, H., Cao, S., Wu, J., Cao, Z., & Wang, F. (2019). Identifying Application-Layer DDoS Attacks Based on Request Rhythm Matrices. IEEE Access, 7(2169–3536), 164480–164491. https://ieeexplore.ieee.org/document/8888259
[21] Jia, Y., Zhong, F., Alrawais, A., Gong, B., & Cheng, X. (2020). FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks. IEEE Internet of Things Journal, 7(10), 9552–9562. https://ieeexplore.ieee.org/document/9090824
[22] Wei, Y., Jaccard, J. J., Sabrina, F., Singh, A., Xu, W., & Camtepe, S. (2021). AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification. IEEE Access, 9(2169–3536), 146810–146821. https://ieeexplore.ieee.org/document/9591559
[23] Hussain, F., Abbas, S. G., Pires, I. M., Tanveer, S., fayyaz, U. U., Garcia, N. M., Shah, G. A., & Shahzad, farrukh. (2021). A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks. IEEE Access, 9(2169–3536), 163412 – 163430. https://ieeexplore.ieee.org/document/9627657
[24] Lee, K., Kim, J., Kwon, K. H., Han, Y., & Kim, S. (2008). DDoS Attack Detection Method Using Cluster Analysis. Expert Systems with Applications, 34(3), 1659–1665. https://doi.org/10.1016/j.eswa.2007.01.040
[25] Zekri, M., Kafhali, S. E., Aboutabit, N., & Saadi, Y. (2017). DDoS Attack Detection Using Machine Learning Techniques in Cloud Computing Environments. 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), 1–7. https://doi.org/10.1109/CloudTech.2017.8284731
------------------------------
[26] Nugraha, B., & Murthy, R. N. (2022). Deep Learning-Based Slow DDoS Attack Detection in SDN-Based Networks. 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 51–56. https://doi.org/10.1109/NFV-SDN50289.2020.9289894
[27] Chen, Y., Pei, J., & Li, D. (2019). DETPro: A High-Efficiency and Low-Latency System Against DDoS Attacks in SDN Based on Decision Tree. ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 1938–1883, 1–6. https://doi.org/10.1109/ICC.2019.8761580
[28] Sudar, K. M., Beulah, M., Deepalakshmi, P., Nagaraj, P., & Chinnasamy, P. (2021). Detection of Distributed Denial of Service Attacks in SDN Using Machine Learning Techniques. 2021 International Conference on Computer Communication and Informatics (ICCCI), 2329–7190, 1–5. https://doi.org/10.1109/ICCCI50826.2021.9402517
[29] Yang, L., & Zhao, H. (2018). DDoS Attack Identification and Defense Using SDN Based on Machine Learning Method. 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN), 2375-527X, 174–178. https://doi.org/10.1109/I-SPAN.2018.00036
[30] Carvalho, R. N., Costa, L. R., Bordim, J. L., & Alchieri, E. A. p. (2021). Detecting DDoS Attacks on SDN Data Plane with Machine Learning. 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW), 138–144. https://doi.org/10.1109/CANDARW53999.2021.00030
[31] Khashab, F., Moubarak, J., Feghali, A., & Bassil, C. (2021). DDoS Attack Detection and Mitigation in SDN Using Machine Learning. 2021 IEEE 7th International Conference on Network Softwarization (NetSoft), 2693–9789, 395–401. https://doi.org/10.1109/NetSoft51509.2021.9492558
[32] Hong, K., Kim, Y., Choi, H., & Park, J. (2017). SDN-Assisted Slow HTTP DDoS Attack Defense Method. IEEE Communications Letters, 22(4), 688 – 691. https://doi.org/10.1109/LCOMM.2017.2766636
[33] Perez-diaz, J. A., Valdovinos, I. A., Choo, K. R., & Zhu, D. (2020). A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning. IEEE Access, 8, 155859 – 155872. https://doi.org/10.1109/ACCESS.2020.3019330
[34] Dong, S., & Sarem, M. (2019). DDoS Attack Detection Method Based on Improved KNN With the Degree of DDoS Attack in Software-Defined Networks. IEEE Access, 8, 5039 – 5048. https://doi.org/10.1109/ACCESS.2019.2963077
[35] Sahoo, K. S., Tripathy, B. K., Naik, K., Ramasubbareddy, S., Balusamy, B., Khari, M., & Burgos, D. (2020). An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks. IEEE Access, 8, 132502 – 132513. https://doi.org/10.1109/ACCESS.2020.3009733
[36] Yu, Y., Guo, L., Liu, Y., Zheng, J., & Zong, Y. (2018). An Efficient SDN-Based DDoS Attack Detection and Rapid Response Platform in Vehicular Networks. IEEE Access, 6, 44570 – 44579. https://doi.org/10.1109/ACCESS.2018.2854567
[37] Zhou, H., Zheng, Y., Jia, X., & Shu, J. (2023). Collaborative Prediction and Detection of DDoS Attacks in Edge Computing: A Deep Learning-Based Approach with Distributed SDN. Computer Networks, 225(109642).
https://doi.org/10.1016/j.comnet.2023.109642.
[38] Yungaicela-naula, N. M., Vargas-rosales, C., & Perez-diaz, J. A. (2021). SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning. IEEE Access, 9, 108495 – 108512. https://doi.org/10.1109/ACCESS.2021.3101650
[39] Tan, L., Pan, Y., Wu, J., Zhou, J., Jiang, H., & Deng, Y. (2020). A New Framework for DDoS Attack Detection and Defense in SDN Environment. IEEE Access, 8, 161908 – 161919. https://doi.org/10.1109/ACCESS.2020.3021435
[40] Sangodoyin, A. O., Akinsolu, M. O., Pillai, P., & Grout, V. (2021). Detection and Classification of DDoS Flooding Attacks on Software-Defined Networks: A Case Study for the Application of Machine Learning. IEEE Access, 9, 122495 – 122508. https://doi.org/10.1109/ACCESS.2021.3109490
[41] Phan, T. V., & Park, M. (2019). Efficient Distributed Denial-of-Service Attack Defense in SDN-Based Cloud. IEEE Access, 7, 18701 – 18714. https://doi.org/10.1109/ACCESS.2019.2896783
[42] Haider, S., Akhunzada, A., Mustafa, I., Patel, T. B., Fernandez, A., & Choo, kim-kwang Raymond. (2020). A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks. IEEE Access, 8, 53972 – 53983. https://doi.org/10.1109/ACCESS.2020.2976908
[43] Taherian-fard, E., Niknam, T., Sahebi, R., Javidsharifi, M., Kavousi-fard, A., & Aghaei, J. (2022). A Software Defined Networking Architecture for DDoS-Attack in the Storage of Multimicrogrids. IEEE Access, 10, 83802 – 83812. https://doi.org/10.1109/ACCESS.2022.3197283
[44] Khamaiseh, S. Y., Alsmadi, I., & Al-alaj, A. (2020). Deceiving Machine Learning-Based Saturation Attack Detection Systems in SDN. 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 44–50. https://doi.org/10.1109/NFV-SDN50289.2020.9289908
[45] Yeom, S., Choi, C., & Kim, K. (2022). LSTM-Based Collaborative Source-Side DDoS Attack Detection. IEEE Access, 10, 44033 – 44045. https://doi.org/10.1109/ACCESS.2022.3169616
[46] Wang, Y., Hu, T., Tang, G., Xie, J., & Lu, J. (2019). SGS: Safe-Guard Scheme for Protecting Control Plane Against DDoS Attacks in Software-Defined Networking. IEEE Access, 7, 34699 – 34710. https://doi.org/10.1109/ACCESS.2019.2895092
[47] De assis, M. v.o., Novaes, M. P., Zerbini, C. B., Carvalho, luiz F, Abrãao, T., & Proença, M. L. (2018). Fast Defense System Against Attacks in Software Defined Networks. IEEE Access, 6, 69620 – 69639. https://doi.org/10.1109/ACCESS.2018.2878576
[48] Hu, D., Hong, P., & Chen, Y. (2017). FADM: DDoS Flooding Attack Detection and Mitigation System in Software-Defined Networking. GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 1–7. https://doi.org/10.1109/GLOCOM.2017.8254023
[49] Das, T., Hamdan, O. A., Sengupta, S., & Arslan, E. (2022). Flood Control: TCP-SYN Flood Detection for Software-Defined Networks Using OpenFlow Port Statistics. 2022 IEEE International Conference on Cyber Security and Resilience (CSR), 1–8. https://doi.org/10.1109/CSR54599.2022.9850339
[50] Zhou, L., Zhu, Y., Zong, T., & Xiang, Y. (2022). A Feature Selection-Based Method for DDoS Attack Flow Classification. Future Generation Computer Systems, 132, 67–79. https://doi.org/10.1016/j.future.2022.02.006
[51] De assis, M. V. o., Carvalho, L. F., Rodrigues, joel J.p.c., Lloret, J., & Proença jr, M. L. (2020). Near Real-Time Security System Applied to SDN Environments in IoT Networks Using Convolutional Neural Network. Future Generation Computer Systems, 86(106738). https://doi.org/10.1016/j.compeleceng.2020.106738
[52] Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Journal of Machine Learning Research, 17(1), 1-5.
|