Ivan Luksha
ABSTRACT
Several traffic optimization approaches are available nowadays, including compression, packet deduplication, specialized hardware and software solutions, application optimization, and neural network utilization. However, the inclination to approach data to consumers and transfer data to the periphery necessitates the employment of novel (perhaps combining the use of known) ways to handle the problem, while keeping in mind that most traffic is encrypted, and the proposed solution should be “transparent” to end-users. The article presents a technique for isolating encrypted IoT traffic, and a study of its effectiveness using a neural network in encrypted SSL sessions has been carried out.
- Lu Yuqian 2020. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837.Google Scholar
Cross Ref
- Reinsel David, Rydning John and Gantz John. 2021. Worldwide global datasphere forecast, 2021–2025: The world keeps creating more data—now, what do we do with it all.Google Scholar
- Khari Manju, Garg Aditya Kumar, Gandomi Amir H., Gupta Rashmi, Patan Rizwan, and Balusamy Balamurugan. 2019. Securing data in Internet of Things (IoT) using cryptography and steganography techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(1), 73-80.Google Scholar
- Elhoseny Mohamed, and Hassanien Aboul Ella. 2019. Secure data transmission in WSN: an overview. Dynamic wireless sensor networks, 115-143.Google Scholar
- Chen Mingzhe, Challita Ursula, Saad Walid, Yin Changchuan, and Debbah M'erouane. 2019. Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039-3071.Google ScholarDigital Library
- Lopez-Martin Manuel, Carro Belen, Sanchez-Esguevillas Antonio, and Lloret Jaime. 2017. Network traffic classifier with convolutional and recurrent neural networks for Internet of Things. IEEE Access, 5, 18042-18050.Google ScholarCross Ref
- Luo Tie, and Nagarajan Sai G. 2018. Distributed anomaly detection using autoencoder neural networks in WSN for IoT. In 2018 IEEE international conference on communications (ICC), IEEE, 1-6.Google Scholar
- Abdellah Ali, Mahmood Omar Abdulkareem, Kirichek Ruslan, Paramonov Alexander, and Koucheryavy Andrey. 2021. Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet. Future Internet, 13(12), 304.Google ScholarCross Ref
- Smys S., Chen Joy Iong Zong, and Shakya Subarna. 2020. Survey on Neural Network Architectures with Deep Learning. Journal of Soft Computing Paradigm (JSCP), 2(03), 186-194.Google ScholarCross Ref
- Yu Hwanjo. 2009. Support Vector Machine. In: LIU L., ÖZSU M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_557Google Scholar
- Hand David , Smyth Padhraic, and Mannila Heikki. 2001. Principles of data mining. MIT Press, Cambridge, MA, USA.Google Scholar
- Jahwar Alan Fuad, and Abdulazeez Adnan Mohsin. 2020. Meta-heuristic algorithms for k-means clustering: A review. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(7), 12002-12020.Google Scholar
- Lozano Sebastian, Guerrero Fernando, Onieva Luis, and Larraneta Juan. 1998. Kohonen maps for solving a class of location-allocation problems. European Journal of operational research, 108(1), 106-117. https://doi.org/10.1016/S0377-2217(97)00046-5.Google Scholar
- Mor Bhavya, Garhwal Sunita, and Kumar Ajay. 2021. A Systematic Review of Hidden Markov Models and Their Applications. Archives of computational methods in engineering, 28(3).Google Scholar
- Fischer Asja, and Igel Christian. 2014. Training restricted Boltzmann machines: An introduction. Pattern Recognition, 47(1), 25-39. https://doi.org/10.1016/j.patcog.2013.05.025.Google ScholarDigital Library
- Tran Son N, and Garcez Artur S d'Avila. 2016. Deep logic networks: Inserting and extracting knowledge from deep belief networks. IEEE transactions on neural networks and learning systems, 29(2), 246-258. DOI: 10.1109/TNNLS.2016.2603784.Google Scholar
- Open Networking Foundation. 2011. OpenFlow/Software-Defined Networking (SDN). https://www.opennetworking.org/.Google Scholar
- Slepnev Aleksey, Karelin Evgenii, Luksha Ivan, Kirichek Ruslan, Zadorozhnyaya Alina, Makarov Eugene. 2021. Virtualization of Control Systems in IoT using Neural Networks. In 2021 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), IEEE, 236-240.Google Scholar
- Berezkin Aleksandr, Zadorozhnyaya Alina, Kukunin Dmitry, Matveev Daniil, Kraeva Ekaterina. 2021. Models and Methods for Decoding of Error-Correcting Codes based on a Neural. In 2021 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), IEEE, 230-235.Google Scholar
- Dainotti Alberto, Pescape Antonio, and Claffy Kimberly. 2012. Issues and future directions in traffic classification. IEEE network, 26(1), 35-40.Google Scholar
- Tahaei Hamid, Afifi Firdaus, Asemi Adeleh, Zaki Faiz, and Anuar Nor Badrul. 2020. The rise of traffic classification in IoT networks: A survey. Journal of Network and Computer Applications, 154, 102538.Google ScholarCross Ref
- Alzahrani Rami J, and Alzahrani Ahmed. 2021. Survey of Traffic Classification Solution in IoT Networks. International Journal of Computer Applications, 975, 8887.Google Scholar
- Do Phuc Hao, Dinh Truong Duy, Le Tran Duc, Pham Van Dai, Myrova Lyudmila, Kirichek Ruslan. 2021. An Efficient Feature Extraction Method for Attack Classification in IoT Networks. In 2021 13th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT), IEEE, 194-199.Google Scholar
Index Terms
- Method for filtering encrypted traffic using a neural network between an Industrial Internet of things system and Digital Twin
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