O-RAN Open Fronthaul 介面之基於 DQN 的 DDoS 緩解方法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：131

、訪客IP：18.221.13.119

姓名

許舒揚(Shu-Yang Hsu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

O-RAN Open Fronthaul 介面之基於 DQN 的 DDoS 緩解方法
(DQN-based DDoS Mitigation Method for Open Fronthaul Interface in O-RAN)

相關論文

★ 非結構同儕網路上以特徵相似度為基準之搜尋方法	★ 以階層式叢集聲譽為基礎之行動同儕網路拓撲架構
★ 線上RSS新聞資料流中主題性事件監測機制之設計與實作	★ 耐延遲網路下具密度感知的路由方法
★ 整合P2P與UPnP內容分享服務之家用多媒體閘道器：設計與實作	★ 家庭網路下簡易無縫式串流影音播放服務之設計與實作
★ 耐延遲網路下訊息傳遞時間分析與高效能路由演算法設計	★ BitTorrent P2P 檔案系統下載端網路資源之可調式配置方法與效能實測
★ 耐延遲網路中利用訊息編碼重組條件之資料傳播機制	★ 耐延遲網路中基於人類移動模式之路由機制
★ 車載網路中以資料匯集技術改善傳輸效能之封包傳送機制	★ 適用於交叉路口環境之車輛叢集方法
★ 車載網路下結合路側單元輔助之訊息廣播機制	★ 耐延遲網路下以靜態中繼節點（暫存盒）最佳化訊息傳遞效能之研究
★ 耐延遲網路下以動態叢集感知建構之訊息傳遞機制	★ 跨裝置影音匯流平台之設計與實作

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-8-19以後開放)

摘要(中)

5G O-RAN 的開放性使基地台可以由各家廠商的設備所組成，不再只是單一廠商壟斷，其開放接口使基站中的傳輸效能得到很大的改善，但也間接曝露了許多資安問題，如:RIC 的A1 接口、SMO 與RU/DU 的O1 接口及DU 與RU 之間的OpenFronthaul 等，這些開放接口在OSI 2 層及3 層成為DoS 攻擊的目標。5G O-RAN 在7.2x 分離前傳界面，此介面由於加密會引入具有挑戰性的時序要求，因此在OpenFronthaul 中的乙太網上未使用加密安全協定，而在O-RAN 的開放架構下更使DoS 在OSI 2 層攻擊成為可能。攻擊者可能冒充DU 或RU，破壞用戶數據或配置中的兩個端點之一及通過對RU 或Open Fronthaul 介面的攻擊以進行干擾，甚至取得訪問權限，如: 對S-Plane 進行攻擊，偽造主時鐘、刪除PTP 封包引起性能下降；而C/U-Plane遭受攻擊，則可能導致用戶資料被竊取，造成難以估計的損失[1]。O-RAN 聯盟在測試整合規範[2] 也提及在O-RAN Open Fronthaul 中OSI 2 層及3 層的DoS 測試必要性，故，本研究在現今較為廣泛的DDoS 流量攻擊中，針對DDoS flood 及LR-DDoS 攻擊在前傳介面所帶來的影響進行了研究，並提出一個「O-RAN Open Fronthaul 介面之基於DQN 的DDoS 緩解方法」。針對OSI 2 層的乙太網幀的流量進行特徵分析以識別異常流量並攔截。
為了模擬演算法效能，本研究開發了一個O-RAN 前傳發封包產生器，並結合Open5GS、srsRAN 及srsUE 建置O-RAN 仿真平台，以測試所提出的DDoS 防禦演算法在前傳中防禦能力。同時，我們也在實驗中證明了DQN 算法在處理多維度DDoS攻擊情境時，比其它代表性的DDoS 緩解演算法(K-means、SVM 及隨機森林) 的平均檢測率、平均誤報率結果更好；在基站的RAN 中DQN 也能更好的改善DDoS 頻寬占用。

最後，本研究也針對DQN 的不同的狀態及獎勵函數設計方法，以加拿大網路安全研究所提出的網路安全測試資料集進行訓練及測試，其結果除了證明本研究提出的DQN 演算法在真實環境中的DDoS 攻擊防禦能力，也證明了所提出的DQN 設計方法相較於對照組的DQN 設計更具適應環境變化的優勢，即我們的方法能以較佳適應能力，兼具DDoS flood 及LR-DDoS 的攻擊的緩解能力。

摘要(英)

The openness of 5G O-RAN allows base stations to be composed of equipment from various vendors, breaking the monopoly of single manufacturers. This open interface significantly
improves transmission performance within the base station but also indirectly exposes numerous security issues. These include the RIC’s A1 interface, the O1 interface between the SMO and RU/DU, and the Open Fronthaul between the DU and RU.
These open interfaces at OSI layers 2 and 3 become targets for DoS attacks.In the 7.2x split fronthaul interface of 5G O-RAN, encryption introduces challenging timing requirements,
leading to the absence of encryption security protocols over Ethernet in the Open Fronthaul. Under the open architecture of O-RAN, DoS attacks at OSI layer 2 are more feasible. Attackers may impersonate DU or RU, compromising one of the endpoints in user data or configurations, or gaining access to the DU and beyond through attacks on the RU or Open Fronthaul interfaces. For example, an attack on the S-Plane by forging master clocks or deleting PTP packets can cause performance degradation. An attack on the C-Plane or U-Plane could lead to the theft of user data, resulting in incalculable losses[1]. The O-RAN ALLIANCE’s testing and integration specifications[2] also mention the necessity of DoS testing at OSI layers 2 and 3 in O-RAN Open Fronthaul. Therefore, this study investigates the impact of DDoS flood and LR-DDoS attacks on the fronthaul interface under prevalent DDoS traffic attacks. It proposes a ”DQN-based DDoS Mitigation Method for the O-RAN Open Fronthaul Interface.” The study analyzes the characteristics of Ethernet frame traffic at OSI layer 2 to identify and intercept abnormal
traffic.

To simulate algorithm performance, this study developed an O-RAN fronthaul packetgenerator and integrated Open5GS, srsRAN, and srsUE to establish an O-RAN simulationplatform. This platform was used to test the proposed DDoS defense algorithm’s ability to protect the fronthaul. Additionally, we demonstrated in experiments that the DQN algorithm outperforms other representative DDoS mitigation algorithms (K-means, SVM, and Random Forest) in terms of average detection rate and average false positive rate when handling multidimensional DDoS attack scenarios. In the RAN of the base station, DQN also significantly improves bandwidth utilization during DDoS attacks.

Finally, this study also explores various state and reward function design methods for DQN, using a network security test dataset from the Canadian Cybersecurity Research
Institute for training and testing. The results not only demonstrate the defense capability of our proposed DQN algorithm against DDoS attacks in real-world environments
but also show that our DQN design method is more adaptable to environmental changes compared to the control group’s DQN design. Our method exhibits better adaptability and the capability to mitigate both DDoS flood and low-rate DDoS attacks.

Index term：O-RAN Base station, RIC, Open fronthaul, DDoS, Machine Learning.

關鍵字(中)

★ O-RAN 基站
★ 無線接入智能控制器
★ 開放式前傳
★ 分散式阻斷式服務攻擊及機器學習
★ 機器學習

關鍵字(英)

★ O-RAN Base station
★ RIC
★ Open fronthaul
★ DDoS
★ Machine Learning

論文目次

目錄
摘要v
Abstract vi
圖目錄xi
表目錄xv
1 簡介2
2 背景與文獻探討6
2.1 O-RAN 的開放式架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 O-RAN 的背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 O-RAN 的架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.3 O-RAN 中的智能控制器(RIC) . . . . . . . . . . . . . . . . . . . . 10
2.1.4 O-RAN 的機器學習模型部署. . . . . . . . . . . . . . . . . . . . . 11
2.2 O-RAN 中潛在的資安問題. . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 O-RAN 架構下的安全性. . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 O-RAN 開放介面的安全協定. . . . . . . . . . . . . . . . . . . . . 15
2.2.3 Open Fronthaul 的資訊安全. . . . . . . . . . . . . . . . . . . . . . 17
2.3 網路中的DoS 拒絕服務攻擊. . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 DoS 攻擊的形式. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.2 DDoS flood/LR-DDoS 攻擊. . . . . . . . . . . . . . . . . . . . . . 20
2.3.3 O-RAN 中O-FH 的DDoS 攻擊. . . . . . . . . . . . . . . . . . . 22
2.4 DDoS 攻擊緩解方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5 機器學習(ML) 方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5.1 機器學習背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5.2 深度學習. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3 研究方法31
3.1 系統架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 封包產生器. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.1 O-RAN 前傳中的資安測試項目. . . . . . . . . . . . . . . . . . . . 33
3.2.2 Open Fronthaul 的封包. . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.3 封包產生器設計. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.4 封包產生器架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 封包特徵過濾. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4 Entropy 資訊熵. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5 DDoS 攻擊識別. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.5.1 封包特徵分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.5.2 加權分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.3 Deep Q Network 深度強化學習. . . . . . . . . . . . . . . . . . . . 45
3.6 O-RAN 前傳中基於DQN 的DDoS 緩解演算法. . . . . . . . . . . . . . . 48
3.6.1 混淆矩陣(檢測率及誤報率) . . . . . . . . . . . . . . . . . . . . . . 50
3.7 演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.8 流程圖. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 實驗與結果分析54
4.1 實驗環境設計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 實驗環境配置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3 模擬工具. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.3.1 O-RAN 基站仿真平台. . . . . . . . . . . . . . . . . . . . . . . . . 57
4.3.2 O-RAN 前傳封包產生器. . . . . . . . . . . . . . . . . . . . . . . . 60
4.4 實驗參數設計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.4.1 網路環境參數設計. . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.4.2 DQN 模型參數設計. . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.5 針對不同攻擊情境的Entropy 分析. . . . . . . . . . . . . . . . . . . . . . 67
4.5.1 DDoS flood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.5.2 LR-DDoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.5.3 LR-DDoS 的加權分析. . . . . . . . . . . . . . . . . . . . . . . . . 73
4.6 超參數調整影響. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.6.1 學習率(α) 的調整. . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.6.2 衰退率(γ) 的調整. . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.7 實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.7.1 在不同DDoS 來源數下DQN 演算法的檢測率及誤報率. . . . . . 83
4.7.2 不同演算法的檢測率及誤報率. . . . . . . . . . . . . . . . . . . . . 87
4.7.3 在不同緩解演算法下的核網優化. . . . . . . . . . . . . . . . . . . 94
5 DQN 於網路安全測試資料集下的效能96
5.1 在DQN 中不同的狀態及獎勵函數設計. . . . . . . . . . . . . . . . . . . . 97
5.1.1 DQN-Control group 演算法. . . . . . . . . . . . . . . . . . . . . . 99
5.2 DDoS 網路安全資料集測試. . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.2.1 DDoS flood 資料集. . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.2.2 LR-DDoS 資料集測試. . . . . . . . . . . . . . . . . . . . . . . . . 105
5.2.3 DQN 設計方法的差異及效能. . . . . . . . . . . . . . . . . . . . . 109
6 結論與未來研究110
附錄A 112
附錄B 118
參考文獻124

參考文獻

參考文獻
[1] Docs,wg11：security work group,o-ran security threat modeling and risk assessment 1.0,o-ran.wg11.threat-modeling.o-r003-v01.00. [Online]. Available:
https://orandownloadsweb.azurewebsites.net/specifications
[2] Docs,tifg：test & integration focus group,o-ran end-to-end test specification
4.0,o-ran.tifg.e2e-test.0-v04.00. [Online]. Available: https://orandownloadsweb. azurewebsites.net/specifications
[3] M. Polese, L. Bonati, S. D＇oro, S. Basagni, and T. Melodia, “Understanding oran: Architecture, interfaces, algorithms, security, and research challenges,” IEEE
Communications Surveys & Tutorials, 2023.
[4] T. Radivilova, L. Kirichenko, D. Ageiev, and V. Bulakh, “Classification methods of machine learning to detect ddos attacks,” in 2019 10th IEEE International Conference
on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol. 1, 2019, pp. 207–210.
[5] D. Stiawan, M. E. Suryani, Susanto, M. Y. Idris, M. N. Aldalaien, N. Alsharif, and R. Budiarto, “Ping flood attack pattern recognition using a k-means algorithm in an internet of things (iot) network,” IEEE Access, vol. 9, pp. 116 475–116 484, 2021.
[6] S. Dong and M. Sarem, “Ddos attack detection method based on improved knn with the degree of ddos attack in software-defined networks,” IEEE Access, vol. 8, pp. 5039–5048, 2020.
[7] N. Zhang, F. Jaafar, and Y. Malik, “Low-rate dos attack detection using psd based entropy and machine learning,” in 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), 2019, pp. 59–62.
[8] Z. Liu, X. Yin, and Y. Hu, “Cpss lr-ddos detection and defense in edge computing utilizing dcnn q-learning,” IEEE Access, vol. 8, pp. 42 120–42 130, 2020.
[9] Cic-ddos2019 dataset. [Online]. Available: https://data.mendeley.com/datasets/ssnc74xm6r/1
[10] Network intrusion dataset(cic-ids- 2017). [Online]. Available: https://www.kaggle.com/datasets/chethuhn/network-intrusion-dataset
[11] J. Navarro-Ortiz, P. Romero-Diaz, S. Sendra, P. Ameigeiras, J. J. Ramos-Munoz, and J. M. Lopez-Soler, “A survey on 5g usage scenarios and traffic models,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 905–929, 2020.
[12] M. Beshley, N. Kryvinska, and H. Beshley, “Energy-efficient qoe-driven radio resource management method for 5g and beyond networks,” IEEE Access, vol. 10, pp. 131 691–131 710, 2022.
[13] D. López-Pérez, A. De Domenico, N. Piovesan, G. Xinli, H. Bao, S. Qitao, and M. Debbah, “A survey on 5g radio access network energy efficiency: Massive mimo, lean carrier design, sleep modes, and machine learning,” IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 653–697, 2022.
[14] A. Mamane, M. Fattah, M. El Ghazi, M. El Bekkali, Y. Balboul, and S. Mazer,“Scheduling algorithms for 5g networks and beyond: Classification and survey,” IEEE Access, vol. 10, pp. 51 643–51 661, 2022.
[15] S. Manap, K. Dimyati, M. N. Hindia, M. S. Abu Talip, and R. Tafazolli, “Survey of radio resource management in 5g heterogeneous networks,” IEEE Access, vol. 8, pp. 131 202–131 223, 2020.
[16] A. Gupta and R. K. Jha, “A survey of 5g network: Architecture and emerging technologies,” IEEE Access, vol. 3, pp. 1206–1232, 2015.
[17] Docs,wg1：use cases and overall architecture workgroup,o-ran architecture description 10.0,o-ran.wg1.oad-r003-v10.00. [Online]. Available: https://orandownloadsweb.azurewebsites.net/specifications
[18] F. W. Murti, J. A. Ayala-Romero, A. Garcia-Saavedra, X. Costa-Pérez, and G. Iosifidis, “An optimal deployment framework for multi-cloud virtualized radio access
networks,” IEEE Transactions on Wireless Communications, vol. 20, no. 4, pp. 2251–2265, 2021.
[19] A. Arnaz, J. Lipman, M. Abolhasan, and M. Hiltunen, “Toward integrating intelligence and programmability in open radio access networks: A comprehensive survey,”IEEE Access, vol. 10, pp. 67 747–67 770, 2022.
[20] Docs,wg6：cloudification and orchestration workgroup,o-ran cloud architecture and deployment scenarios for o-ran virtualized ran 5.0,o-ran.wg6.cads-v05.00. [Online].
Available:https://orandownloadsweb.azurewebsites.net/specifications
[21] Docs,wg3：near-real-time ric and e2 interface workgroup,o-ran near-rt ric architecture 5.0,o-ran.wg3.ricarch-r003-v05.00. [Online]. Available: https:
//orandownloadsweb.azurewebsites.net/specifications
[22] Docs,wg2：non-real-time ran intelligent controller and a1 interface workgroup,wg2: Non-real-time ran intelligent controller and a1 interface workgroup,o-ran non-rt ric:
Functional architecture 1.01,o-ran.wg2.non-rt-ric-arch-tr v01.01. [Online]. Available:
https://orandownloadsweb.azurewebsites.net/specifications
[23] L. Bonati, S. D’Oro, M. Polese, S. Basagni, and T. Melodia, “Intelligence and learning in o-ran for data-driven nextg cellular networks,” IEEE Communications Magazine,
vol. 59, no. 10, pp. 21–27, 2021.
[24] Docs,wg2：non-real-time ran intelligent controller and a1 interface workgroup,o-ran ai/ml workflow description and requirements 1.03,o-ran.wg2.aiml-v01.03. [Online].
Available: https://orandownloadsweb.azurewebsites.net/specifications
[25] W. Tiberti, E. Di Fina, A. Marotta, and D. Cassioli, “Impact of man-in-the-middle attacks to the o-ran inter-controllers interface,” in 2022 IEEE Future Networks World
Forum (FNWF), 2022, pp. 367–372.
[26] D. Dik and M. S. Berger, “Open-ran fronthaul transport security architecture and implementation,” IEEE Access, vol. 11, pp. 46 185–46 203, 2023.
[27] Docs,security work group：o-ran security requirements and controls specification 8.0. [Online]. Available: https://orandownloadsweb.azurewebsites.net/specifications
[28] M. Tayyab, B. Belaton, and M. Anbar, “Icmpv6-based dos and ddos attacks detection using machine learning techniques, open challenges, and blockchain applicability: A review,” IEEE Access, vol. 8, pp. 170 529–170 547, 2020.
[29] K. Suto, H. Nishiyama, N. Kato, T. Nakachi, T. Fujii, and A. Takahara, “Thup: A p2p network robust to churn and dos attack based on bimodal degree distribution,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 9, pp. 247–256, 2013.
[30] F. Yihunie, E. Abdelfattah, and A. Odeh, “Analysis of ping of death dos and ddos attacks,” in 2018 IEEE Long Island Systems, Applications and Technology Conference (LISAT), 2018, pp. 1–4.
[31] S. T. Zargar, J. Joshi, and D. Tipper, “A survey of defense mechanisms against distributed denial of service (ddos) flooding attacks,” IEEE communications surveys & tutorials, vol. 15, no. 4, pp. 2046–2069, 2013.
[32] D. Drinić and Z. Čiča, “Survey on low-rate ddos attacks, detection and defense,” in 2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH), 2024, pp. 1–6.
[33] H. Lotfalizadeh and D. S. Kim, “Investigating real-time entropy features of ddos attack based on categorized partial-flows,” in 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2020, pp. 1–6.
[34] K. S. Sahoo, B. K. Tripathy, K. Naik, S. Ramasubbareddy, B. Balusamy, M. Khari, and D. Burgos, “An evolutionary svm model for ddos attack detection in software
defined networks,” IEEE Access, vol. 8, pp. 132 502–132 513, 2020.
[35] U. Garg, M. Kaur, M. Kaushik, and N. Gupta, “Detection of ddos attacks using semisupervised based machine learning approaches,” in 2021 2nd International Conference on Computational Methods in Science & Technology (ICCMST). IEEE, 2021, pp. 112–117.
[36] Ismail, M. I. Mohmand, H. Hussain, A. A. Khan, U. Ullah, M. Zakarya, A. Ahmed, M. Raza, I. U. Rahman, and M. Haleem, “A machine learning-based classification and prediction technique for ddos attacks,” IEEE Access, vol. 10, pp. 21 443–21 454, 2022.
[37] N. Niknami and J. Wu, “Entropy-kl-ml:enhancing the entropy-kl-based anomaly detection on software-defined networks,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 6, pp. 4458–4467, 2022.
[38] J. A. Pérez-Díaz, I. A. Valdovinos, K.-K. R. Choo, and D. Zhu, “A flexible sdnbased architecture for identifying and mitigating low-rate ddos attacks using machine learning,” IEEE Access, vol. 8, pp. 155 859–155 872, 2020.
[39] T. R. N and R. Gupta, “A survey on machine learning approaches and its techniques:,” in 2020 IEEE International Students’ Conference on Electrical,Electronics and Computer Science (SCEECS), 2020, pp. 1–6.
[40] K. Nugroho, E. Noersasongko, Purwanto, Muljono, A. Z. Fanani, Affandy, and R. S. Basuki, “Improving random forest method to detect hatespeech and offensive word,” in 2019 International Conference on Information and Communications Technology (ICOIACT), 2019, pp. 514–518.
[41] R. Khaoula and M. Mohamed, “Improving intrusion detection using pca and k-means clustering algorithm,” in 2022 9th International Conference on Wireless Networks and
Mobile Communications (WINCOM), 2022, pp. 1–5.
[42] Z. Zhong, J. Li, D. A. Clausi, and A. Wong, “Generative adversarial networks and conditional random fields for hyperspectral image classification,” IEEE Transactions
on Cybernetics, vol. 50, no. 7, pp. 3318–3329, 2020.
[43] S. Kim, J. Son, A. Talukder, and C. S. Hong, “Congestion prevention mechanism based on q-leaning for efficient routing in sdn,” in 2016 International Conference on Information Networking (ICOIN), 2016, pp. 124–128.
[44] Docs,transport layer and o-ran fronthaul protocol implementation. [Online]. Available: https://docs.o-ran-sc.org/projects/o-ran-sc-o-du-phy/en/latest/ Transport-Layer-and-ORAN-Fronthaul-Protocol-Implementation_fh.html#
[45] X.-F. Song, Y. Zhang, D.-W. Gong, and X.-Z. Gao, “A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for highdimensional data,” IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9573–9586, 2022.
[46] N. Gopika and A. M. Kowshalaya M.E., “Correlation based feature selection algorithm for machine learning,” in 2018 3rd International Conference on Communication and Electronics Systems (ICCES), 2018, pp. 692–695.
[47] K. S. Sahoo, A. Iqbal, P. Maiti, and B. Sahoo, “A machine learning approach for predicting ddos traffic in software defined networks,” in 2018 International Conference
on Information Technology (ICIT), 2018, pp. 199–203.
[48] M. A. Setitra, I. Benkhaddra, Z. E. Abidine Bensalem, and M. Fan, “Feature modeling and dimensionality reduction to improve ml-based ddos detection systems in sdn environment,” in 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2022, pp. 1–7.
[49] M. H. Bhuyan and E. Elmroth, “Multi-scale low-rate ddos attack detection using the generalized total variation metric,” in 2018 17th IEEE International Conference on
Machine Learning and Applications (ICMLA), 2018, pp. 1040–1047.
[50] J. A. Pérez-Díaz, I. A. Valdovinos, K.-K. R. Choo, and D. Zhu, “A flexible sdnbased architecture for identifying and mitigating low-rate ddos attacks using machine learning,” IEEE Access, vol. 8, pp. 155 859–155 872, 2020.
[51] C. Alocious, H. Xiao, and B. Christianson, “Analysis of dos attacks at mac layer in mobile adhoc networks,” in 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), 2015, pp. 811–816.
[52] M. Dasari, “Real time detection of mac layer dos attacks in ieee 802.11 wireless networks,” in 2017 14th IEEE annual consumer communications & networking conference (CCNC). IEEE, 2017, pp. 939–944.
[53] M. Chen, J. Ben-Othman, and L. Mokdad, “Novel denial-of-service attacks against lorawan on mac layer,” IEEE Communications Letters, 2023.
[54] B. Gogoi and T. Ahmed, “Http low and slow dos attack detection using lstm based deep learning,” in 2022 IEEE 19th India Council International Conference (INDICON),
2022, pp. 1–6.
[55] R. Van De Meent, M. Mandjes, and A. Pras, “Gaussian traffic everywhere?” in 2006 IEEE International Conference on Communications, vol. 2. IEEE, 2006, pp. 573–578.
[56] R. d. O. Schmidt, R. Sadre, N. Melnikov, J. Schönwälder, and A. Pras, “Linking network usage patterns to traffic gaussianity fit,” in 2014 IFIP Networking Conference.
IEEE, 2014, pp. 1–9.
[57] Y. Purwanto, B. Rahardjo et al., “Statistical analysis on aggregate and flow based traffic features distribution,” in 2015 1st International Conference on Wireless and
Telematics (ICWT). IEEE, 2015, pp. 1–6.
[58] R. Fontugne, P. Abry, K. Fukuda, D. Veitch, K. Cho, P. Borgnat, and H. Wendt, “Scaling in internet traffic: a 14 year and 3 day longitudinal study, with multiscale
analyses and random projections,” IEEE/ACM Transactions on Networking, vol. 25, no. 4, pp. 2152–2165, 2017.
[59] J. Gonzalez and C. A. Bollmann, “Aggregated impulses: Towards explanatory models for self-similar alpha stable network traffic,” in 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2019, pp. 1–10.

指導教授

胡誌麟(Chih-Lin Hu)

審核日期

2024-8-20

推文