Exploring the Synergy: A Review of Machine Learning Techniques in Software Defined Networking (SDN) (2024)

Open Access

Issue

ITM Web Conf.

Volume 64, 2024

2nd International Conference on Applied Computing & Smart Cities (ICACS24)
Article Number01016
Number of page(s)23
DOIhttps://doi.org/10.1051/itmconf/20246401016
Published online05 July 2024
  1. Clark, D. D., Partridge, C., Christopher Ramming, J., & Wroclawski, J. T., A Knowledge Plane for the Internet. Computer Communication Review, 33(4), 3-10, 2003. [GoogleScholar]
  2. Mestres, A., Rodriguez-Natal, A., Carner, J., Barlet-Ros, P., Alarcn, E., Sol, M., MuntsMulero, V., Meyer, D., Barkai, S., Hibbett, M. J., Estrada, G., Ma ’ruf, K., Coras, F., Ermagan, V., Latapie, H., Cassar, C., Evans, J., Maino, F., Walrand, J., Muntes-Mulero, V. (2017). Knowledge-Defined Networking Artifacts Review for Knowledge-Defined Networking Knowledge-Defined Networking. ACM SIGCOMM Computer Communication Review, 47(3), 2-10, 2017. [GoogleScholar]
  3. Ali, O. M. A., Kareem, S. W., & Mohammed, A. S., Evaluation of electrocardiogram signals classification using CNN, SVM, and LSTM algorithm : A review. In 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) (pp. 185-191). IEEE, 2022. [GoogleScholar]
  4. Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J., Machine learning for networking: Workflow, advances and opportunities. IEEE Network, 32(2), 92-99, 2017. [GoogleScholar]
  5. Ma, R., Kareem, S. W., Kalra, A., Doewes, R. I., Kumar, P., & Miah, S., Optimization of electric automation control model based on artificial intelligence algorithm. Wireless Communications and Mobile Computing, 2022. [GoogleScholar]
  6. Guibao, X., Yubo, M., & Jialiang, L., The impact of Artificial Intelligence on communication networks and services. ITUJournal, 1(1), 33-38, 2018. [GoogleScholar]
  7. CEKIC, J., & DUJANOVIC, P., Osnovi Metodologije Planiranja Zdravstvene Za Stite. Higijena; Casopis Za Higijenu, Mikrobiologiju, Epidemiologiju i Sanitamu Tehniku, 15(1), 3–15, 1963 [GoogleScholar]
  8. Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R., Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2), 493–501, 2019. [GoogleScholar]
  9. Latah, M., & Toker, L., Artificial intelligence enabled software-defined networking: A comprehensive overview. IET Networks, 8(2), 79–99, 2019 [GoogleScholar]
  10. Rusek, K., Suarez-Varela, J., Almasan, P., Barlet-Ros, P., & Cabellos-Aparicio, A., RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN. IEEE Journal on Selected Areas in Communications, 38(10), 2260–2270, 2020. [GoogleScholar]
  11. Mikhail, Dina Yousif, Roojwan Sc Hawezi, and Shahab WahhabKareem., “An Ensemble Transfer Leaming Model for Detecting Stego Images.”, Applied Sciences 13.12: 7021, 2023. [GoogleScholar]
  12. Kurochkin, I. I., & Volkov, S. S., Using GRU based deep neural network for intrusion detection in software-defined networks. IOP Conference Series: Materials Science and Engineering, 927(1), 2020. [GoogleScholar]
  13. Heo, D., Lange, S., Kim, H.-G., & Choi, H., Graph Neural Network based Service Function Chaining for Automatic Network Control. 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS), 7–12, 2020. [GoogleScholar]
  14. Mohd Amiruddin, A. A. A., Zabiri, H., Jeremiah, S. S., Teh, W. K., & Kamaruddin, B., Valve stiction detection through improved pattern recognition using neural networks. Control Engineering Practice, 90(May), 63–84, 2019. [GoogleScholar]
  15. Chen, X. F., & Yu, S. Z., CIPA: A collaborative intrusion prevention architecture for programmable network and SDN. Computers and Security, 58, 1–19, 2016. [GoogleScholar]
  16. He, M., Kalmbach, P., Blenk, A., Kellerer, W., & Schmid, S., Algorithm-data driven optimization of adaptive communication networks. Proceedings International Conference on Network Protocols, ICNP, 2017. [GoogleScholar]
  17. Alvizu, R., Troia, S., Maier, G., & Pattavina, A., Matheuristic with machine-learning­ based prediction for software-defined mobile metro-core networks. Journal of Optical Communications and Networking, 9(9), D19–D30, 2017 [GoogleScholar]
  18. Abubakar, A., & Pranggono, B., Feminismo masculino. 2015. [GoogleScholar]
  19. Sabbeh, A., Al-Dunainawi, Y., Al-Raweshidy, H. S., & Abbod, M. F., Performance prediction of software defined network using an artificial neural network. Proceedings of 2016 SAI Computing Conference, SAI 2016, 80–84, 2016 [GoogleScholar]
  20. Mihai-Gabriel, I., & Victor-Valeriu, P., Achieving DDoS resiliency in a software defined network by intelligent risk assessment based on neural networks and danger theory. CINTI 2014 15th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings, 319–324, 2014 [GoogleScholar]
  21. Sahoo, K. S., Tripathy, B. K., Naik, K., Member, S., & Ramasubbareddy, S., An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks. 2020. [GoogleScholar]
  22. Aslam, M., Ye, D., Hanif, M., & Asad, M., Machine Learning Based SON-enabled Distributed Denial-of-Services Attacks Detection and Mitigation System for Internet of Things. International Conference on Machine Learning for Cyber Security, 180–194, 2020. [GoogleScholar]
  23. Zhao, J., Zeng, P., Shang, W., & Tong, G., DDoS Attack Detection Based on One-Class SVM in SDN. International Conference on Artificial Intelligence and Security, 189–200, 2020. [GoogleScholar]
  24. Kyaw, A. T., Zin Oo, M., & Khin, C. S., Machine-Learning Based DDOS Attack Classifier in Software Defined Network. 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020, 431–434, 2020. [GoogleScholar]
  25. Aung, K. M., & Htaik, N. M., Anomaly Detection in SDN’s Control Plane using Combining Entropy with SVM. 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020, 122–126, 2020. [GoogleScholar]
  26. Phan, T. V., Bao, N. K., & Park, M., A Novel Hybrid Flow-Based Handler with DDoS Attacks in Software-Defined Networking. Proceedings 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016, 350–357, 2017. [GoogleScholar]
  27. Shang, G., Zhe, P., Bin, X., Aiqun, H., & Kui, R., Flood Defender: Protecting data and control plane resources under SDN-aimed DoS attacks. Proceedings IEEE INFOCOM, 2017. [GoogleScholar]
  28. Hu, D., Hong, P., & Chen, Y., FADM: DDoS Flooding Attack Detection and Mitigation System in Software-Defined Networking, 2017 IEEE 30 Global Communications Conference, GLOBECOM 2017 Proceedings, 1–7, 2018-January [GoogleScholar]
  29. Rego, A., Canovas, A., Jimenez, J. M., & Lloret, J., An Intelligent System for Video Surveillance in IoT Environments. IEEE Access, 6(c), 31580–31598, 2018. [GoogleScholar]
  30. Ali, M. A., Kareem, S. W., & Mohammed, A. S., Comparative evaluation for two and five classes ECG signal classification: applied deep learning. Journal of Algebraic Statistics, 13(3), 580–596, 2022. [GoogleScholar]
  31. Mertens, J. S., Milotta, G. M., Nagaradjane, P., & Morabito, G., SDN-(UAV)ISE: Applying Software Defined Networking to Wireless Sensor Networks with Data Mules. 323–328, 2020. [GoogleScholar]
  32. Owusu, A. I., & Nayak, A., An Intelligent Traffic Classification in SDN-IoT: A Machine Leaming Approach. 2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), 1–6, 2020. [GoogleScholar]
  33. Reticcioli, E., Di Girolamo, G. D., Smarra, F., Carmenini, A., D’Innocenzo, A., & Graziosi, F., Leaming SDN traffic flow accurate models to enable queue bandwidth dynamic optimization. 2020 European Conference on Networks and Communications, EuCNC 2020, 231–235. 2020. [GoogleScholar]
  34. Abbasi, M., Rezaei, H., Menon, V. G., Qi, L., & Khosravi, M. R., Enhancing the Performance of Flow Classification in SDN-Based Intelligent Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, 1–10, 2020. [GoogleScholar]
  35. Balta, M., & Ozi;elik, İ., A 3-stage fuzzy-decision tree model for traffic signal optimization in urban city via a SDN based VANET architecture. Future Generation Computer Systems, 104, 142–158, 2020. [GoogleScholar]
  36. Le, A., Dinh, P., Le, H., & Tran, N. C., Flexible Network-Based Intrusion Detection and Prevention System on Software-Defined Networks. 31 Proceedings 2015 International Conference on Advanced Computing and Applications, ACOMP 2015, 106–111, 2016. [GoogleScholar]
  37. Nagarathna, R., & Shalinie, S. M., SLAMHHA: A supervised learning approach to mitigate host location hijacking attack on SDN controllers. 2017 4th International Conference on Signal Processing, Communication and Networking, ICSCN 2017. [GoogleScholar]
  38. Tariq, F., & Baig, S., Botnet classification using centralized collection of network flow counters in software defined networks. International Journal of Computer Science and Information Security, 14(8), 1075–1080, 2016. [GoogleScholar]
  39. Stimpfling, T., Belanger, N., Cherkaoui, 0., Beliveau, A., Beliveau, L., & Savaria, Y., Extensions to decision-tree based packet classification algorithms to address new classification paradigms. Computer Networks, 122, 83–95, 2017. [GoogleScholar]
  40. Tsogbaatar, E., Bhuyan, M. H., Taenaka, Y., Fall, D., Gonchigsumlaa, K., Elmroth, E., & Kadobayashi, Y., SON-Enabled IoT Anomaly Detection Using Ensemble Leaming. In IFIP Advances in Information and Communication Technology: Vol. 584 IFIP. Springer International Publishing, 2020. [GoogleScholar]
  41. Miao, M., & Wu, B., A Flexible Phishing Detection Approach Based on Software­ Defined Networking Using Ensemble Leaming Method. Proceedings of the 2020 4th International Conference on High Performance, Compilation, Computing and Communications, 7073, 2020. [GoogleScholar]
  42. Abar, T., Ben Letaifa, A., & El Asmi, S., Machine learning based QoE prediction in SDN networks. 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017, 1395–1400, 2017. [GoogleScholar]
  43. Amaral, P., Dinis, J., Pinto, P., Bernardo, L., Tavares, J., & Mamede, H. S., Machine learning in software defined networks: Data collection and traffic classification. Proceedings International Conference on Network Protocols, ICNP, 2016 December (Network:ML), 91–95, 2016. [GoogleScholar]
  44. Zago, M., Ruiz Sanchez, V. M., Gil Perez, M., & Martinez Perez, G., Tackling Cyber Threats with Automatic Decisions and Reactions Based on Machine-Leaming Techniques. Presented at EuCNC’ 17: 2nd Conference on Network Management, Quality of Service and Security for 5G Networks, Held at European Conference on Networks and Communications, At Oulu (Finland), September 2017. [GoogleScholar]
  45. Su, S. C., Chen, Y. R., Tsai, S. C., & Lin, Y. B., Detecting P2P Botnet in Software Defined Networks. Security and Communication Networks, 2018. [GoogleScholar]
  46. Chen, Z., Jiang, F., Cheng, Y., Gu, X., Liu, W., & Peng, J., XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud. Proceedings 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018, 251–256, 2018. [GoogleScholar]
  47. Choudhury, G., Lynch, D., Thakur, G., & Tse, S., Two use cases of machine learning for SON-Enabled IP/Optical networks: Traffic matrix prediction and optical path performance prediction. ArXiv, 10(10), 52–62, 2018. [GoogleScholar]
  48. Karwan, M., Abdullah, S., Amin, A. M., Mohamed, Z. A., Bestoon, B., Shekha, M., & Salihi, A., Cancer incidence in the Kurdistan region of Iraq: Results of a seven-year cancer registration in Erbil and Duhok Govemorates. Asian Pacific Journal of Cancer Prevention: APJCP, 23(2), 601, 2022. [GoogleScholar]
  49. Said Elsayed, M., Le-Khac, N.-A., Dev, S., & Jurcut, A. D., Network Anomaly Detection Using LSTM Based Autoencoder. Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, 37–45, 2020. [GoogleScholar]
  50. Raikar, M. M., Meena, S. M., Mulla, M. M., Shetti, N. S., & Karanandi, M., Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning. Procedia Computer Science, 171(2019), 2750–2759, 2020. [GoogleScholar]
  51. Shu, J., Zhou, L., Zhang, W., Du, X., & Guizani, M., Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach. IEEE Transactions on Intelligent Transportation Systems, 1–12, 2020. [GoogleScholar]
  52. Malik, A., De Frein, R., Al-Zeyadi, M., & Andreu-Perez, J., Intelligent SDN Traffic Classification Using Deep Leaming: Deep-SDN. 2020 2nd International Conference on Computer Communication and the Internet, ICCCI 2020, 184–189, 2020. [GoogleScholar]
  53. Tang, T. A., Mhamdi, L., Mclernon, D., Zaidi, S. A. R., & Ghogho, M., Deep learning approach for Network Intrusion Detection in Software Defined Networking. Proceedings 2016 International Conference on Wireless Networks and Mobile Communications, WINCOM 2016: Green Communications and Networking, 258–263, 2016. [GoogleScholar]
  54. Lazaris, A., & Prasanna, V. K., Deep Flow: A deep learning framework for software­ defined measurement. CAN 2017 Proceedings of the 2017 Cloud-Assisted Networking Workshop, Part of CoNext 2017, 43–48, 2017. [GoogleScholar]
  55. Ivannikova, E., Zolotukhin, M., & Hamalainen, T., Probabilistic transition-based approach for detecting application-layer DDoS Attacks in encrypted software-defined networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10394 LNCS, 531–543, 2017. [GoogleScholar]
  56. Nguyen, T. M. T., Hamidouche, L., Mathieu, F., Monnet, S., & Iskounen, S., SDN-based Wi-Fi Direct clustering for cloud access in campus networks. Annales Des Telecommunications/Annals of Telecommunications, 73(3-4), 239–249, 2018. [GoogleScholar]
  57. Bakhshi, T., & Ghita, B., OpenFlow-enabled user traffic profiling in campus software defined networks. International Conference on Wireless and Mobile Computing, Networking and Communications, 2016. [GoogleScholar]
  58. Harikrishna, P., & Amuthan, A., SDN-based DDoS Attack Mitigation Scheme using Convolution Recursively Enhanced Self Organizing Maps. Sadhana Academy Proceedings in Engineering Sciences, 45(1), 2020. [GoogleScholar]
  59. Jankowski, D., & Amanowicz, M., Intrusion detection in software defined networks with self-organized maps. Journal of Telecommunications and Information Technology, 2015(4), 3–9, 2015. [GoogleScholar]
  60. Wang, T., & Chen, H., SGuard: A lightweight SDN safe-guard architecture for DoS attacks. China Communications, 14(6), 113–125, 2017. [GoogleScholar]
  61. Phan, T. V., Bao, N. K., & Park, M., Distributed-SOM: A novel performance bottleneck handler for large-sized software-defined networks under flooding attacks. Journal of Network and Computer Applications, 91, 14–25, 2017. [GoogleScholar]
  62. Braga, R., Mota, E., & Passito, A., Lightweight DDoS flooding attack detection using NOX/OpenFlow. Proceedings Conference on Local Computer Networks, LCN, 408415, 2010. [GoogleScholar]
  63. Huang, G., & Youn, H. Y. (2020). Proactive eviction of flow entry for SDN based on hidden Markov model. Frontiers of Computer Science, 14(4), 1–10, 2020. [GoogleScholar]
  64. Prasanth, L. L., & Uma, E., Hidden Markov Model Based Secure Cluster Management in Software Defined Networking. 5, 123–126, 2020. [GoogleScholar]
  65. Yang, Y., & Sun, H., Research on Traffic Optimization Scheme of SDN Network Based on ME-HMM. Journal of Physics: Conference Series, 1624, 042052, 2020. [GoogleScholar]
  66. Fan, Z., Xiao, Y., Nayak, A., & Tan, C., An improved network security situation assessment approach in software defined networks. Peer-to-Peer Networking and Applications, 12(2), 295–309, 2019. [GoogleScholar]
  67. Shan-Shan, J., & Ya-Bin, X., The APT detection method in SDN. 2017 3rd IEEE International Conference on Computer and Communications, ICCC 36, 2018-January [GoogleScholar]
  68. Das, D., Bapat, J., & Das, D., Unsupervised Leaming Based Capacity Augmentation in SDN Assisted Wireless Networks. SN Computer Science, 1(4), 2020. [GoogleScholar]
  69. Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, 0., Inoue, T., & Mizutani, K., Routing or Computing? the Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Leaming. IEEE Transactions on Computers, 66(11), 1946–1960, 2017. [GoogleScholar]
  70. Zhang, C., Wang, X., Li, F., He, Q., & Huang, M., Deep learning-based network application classification for SDN. Transactions on Emerging Telecommunications Technologies, 29(5), 2018. [GoogleScholar]
  71. Niyaz, Q., Sun, W., & Javaid, A. Y., A Deep Leaming Based DDoS Detection System in Software-Defined Networking (SDN). ICST Transactions on Security and Safety, 4(12), 2017. [GoogleScholar]
  72. Liu, W. X., Zhang, J., Liang, Z. W., Peng, L. X., & Cai, J. (2017). Content Popularity Prediction and Caching for ICN: A Deep Leaming Approach with SDN. IEEE Access, 6(c), 5075–5089. https://doi.org/10.1109/ACCESS.2017.2781716 [GoogleScholar]
  73. Ahmed, M. E., Kim, H., & Park, M., Mitigating DNS query-based DDoS attacks with machine learning on software-defined networking. Proceedings IEEE Military Communications Conference MILCOM, 2017-October, 11–16, 2017. [GoogleScholar]
  74. Zolotukhin, M., Kumar, S., & Hamalainen, T., Reinforcement learning for attack mitigation in SDN-enabled networks. Proceedings of the 2020 IEEE 37, Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020, 282–286, 2020. [GoogleScholar]
  75. Sendra, S., Rego, A., Lloret, J., Jimenez, J. M., & Romero, 0., Including artificial intelligence in a routing protocol using Software Defined Networks. 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017, Scpa, 670–674, 2017. [GoogleScholar]
  76. Uzakgider, T., Cetinkaya, C., & Sayit, M., Leaming-based approach for layered adaptive video streaming over SDN. Computer Networks, 92, 357–368, 2015. [GoogleScholar]
  77. Stampa, G., Arias, M., Sanchez-Charles, D., Muntes-Mulero, V., & Cabellos, A., A deep­ reinforcement learning approach for software-defined networking routing optimization. ArXiv, 14–16, 2017. [GoogleScholar]
  78. Ravi, N., & Shalinie, S. M., Leaming-Driven Detection and Mitigation of DDoS Attack in IoT via SDN-Cloud Architecture. IEEE Internet of Things Journal, 7(4), 3559–3570, 2020. [GoogleScholar]
  79. Chen, F., & Zheng, X., Machine-learning based routing pre-plan for SDN. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9426, 149–159, 2015. [GoogleScholar]
  80. Loog, M., Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 462–475, 2016. [GoogleScholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

Exploring the Synergy: A Review of Machine Learning Techniques in Software Defined Networking (SDN) (2024)

References

Top Articles
A Beginner’s Guide to Working with Clay – Blue Era
Jack Williams Inspection Coupons
No Hard Feelings Showtimes Near Metropolitan Fiesta 5 Theatre
Touchstar Cinemas - Sabal Palms Products
All Obituaries | Sneath Strilchuk Funeral Services | Funeral Home Roblin Dauphin Ste Rose McCreary MB
Shadle Park big-play combo of Hooper-to-Boston too much for Mt. Spokane in 20-16 win
Opsahl Kostel Funeral Home & Crematory Yankton
Craigslist Greenville Pets Free
Nycers Pay Schedule
Irissangel
Grizzly Expiration Date 2023
5 high school boys cross country stars of the week: Sept. 13 edition
Pathfinder 2E Throwing Weapons
Five Guys Calorie Calculator
Kaelis Dahlias
M3Gan Showtimes Near Regal City North
Onlybaddiestv
Dirt Devil Ud70181 Parts Diagram
Persona 5 R Fusion Calculator
Biobased Circular Business Platform
Alloyed Trident Spear
Telegram Voyeur
Act3: Walkthrough | Divinity Original Sin 2 Wiki
Disney Cruise Line
We analyzed every QAnon post on Reddit. Here’s who QAnon supporters actually are.
The Star Beacon Obituaries
Ullu Web Series 123
Squeezequeens
Orileys Auto Near Me
How to Learn Brazilian Jiu‐Jitsu: 16 Tips for Beginners
Used Zero Turn Mowers | Shop Used Zero Turn Mowers for Sale - GSA Equipment
Jeep Graphics Ideas
Wolf Of Wallstreet 123 Movies
Rate My Naughty.com
20 Fantastic Things To Do In Nacogdoches, The Oldest Town In Texas
Panama City News Herald Obituary
Glassbox Eyecare
Indiana Immediate Care.webpay.md
Things To Do in Sanford, Florida - Historic Downtown Sanford
Mudfin Village Questline
Alfyn Concoct
Of Course! havo/vwo bovenbouw
Edenmodelsva
Whitfield County Jail Inmates P2C
Depths Charm Calamity
What Happened To Daniel From Rebecca Zamolo
Windows 10 schnell und gründlich absichern
Senna Build Guides :: League of Legends Strategy Builds, Runes, Items, and Abilities :: Patch 14.18
German police arrest 25 suspects in plot to overthrow state – DW – 12/07/2022
18006548818
When His Eyes Opened Chapter 3002
Latest Posts
Article information

Author: Velia Krajcik

Last Updated:

Views: 6253

Rating: 4.3 / 5 (74 voted)

Reviews: 81% of readers found this page helpful

Author information

Name: Velia Krajcik

Birthday: 1996-07-27

Address: 520 Balistreri Mount, South Armand, OR 60528

Phone: +466880739437

Job: Future Retail Associate

Hobby: Polo, Scouting, Worldbuilding, Cosplaying, Photography, Rowing, Nordic skating

Introduction: My name is Velia Krajcik, I am a handsome, clean, lucky, gleaming, magnificent, proud, glorious person who loves writing and wants to share my knowledge and understanding with you.