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. |