Protecting Software Defined Networks with IoT and Deep Reinforcement Learning
Keywords:
Internet of Things (IoT), Software-Defined Network (SDN), LightGBM, Multilayer Perception Network, Deep Reinforcement LearningAbstract
The Internet of Things (IoT) has been seeing rapid expansion for a reason that is quite justifiable. The operators of these devices have already put up a diligent infrastructure. Among the technologies that need to be created to support this kind of sensors are enterprise safety initiatives. This paper discusses the stability routing protocol, which is based on a trustworthy assessment of the equipment and packet flow. To build trustworthy Software-Defined a network (SDN) routes, improve the trust in network a part moves and the Quantity of Service, or QoS, or power conditions. When utilized alongside deep learning algorithms, the DRL-IDS structure described in this the work successfully identifies hacking. It is based on a feature selection technique based on LightGBM, which successfully decides on the most appealing set of characteristics from company Internet of Things data. The application depends on GBM's selection of features system, which takes industrial Internet of Things knowledge and extracts the most compelling feature set; the multilayer perception network's hidden layer is then used along with the deep learning algorithm to create the prevalent network architecture for the valuable network and significant system in the PPO2 algorithm; and lastly, the PPO2 method and ReLU (R) technology are used to create the breach detection model. Comprehensive evaluation on a publicly available data set demonstrates that the proposed system for intrusion detection is 95 percent accurate in identifying different kinds of network attacks within the Sector the internet of Things. The currently the system is used for detection of intrusions is based on models using deep learning such as LSTM, CNN, and RNN, and extensive reinforcement programming algorithms like DDQN and DQN.
Downloads
References
Harika, J.; Baleeshwar, P.; Navya, K.; Shanmugasundaram, H. A review on artificial intelligence with deep human reasoning. In Proceedings of the 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 9–11 May 2022; pp. 81–84.
Farhan, L.; Hameed, R.S.; Ahmed, A.S.; Fadel, A.H.; Gheth, W.; Alzubaidi, L.; Fadhel, M.A.; Al-Amidie, M. Energy efficiency for green internet of things (IoT) networks: A survey. Network 2021, 1, 279–314.
Almusaylim, Z.A.; Zaman, N. A review on smart home present state and challenges: Linked to context-awareness internet of things (IoT). Wirel. Netw. 2019, 25, 3193–3204.
Amin, F.; Abbasi, R.; Mateen, A.; Ali Abid, M.; Khan, S. A step toward next-generation advancements in the internet of things technologies. Sensors 2022, 22, 8072.
Barnett, G.A.; Ruiz, J.B.; Xu, W.W.; Park, J.Y.; Park, H.W. The world is not flat: Evaluating the inequality in global information gatekeeping through website co-mentions. Technol. Forecast. Soc. Chang. 2017, 117, 38–45.
Alhaj, A.N.; Dutta, N. Analysis of security attacks in SDN network: A comprehensive survey. In Contemporary Issues in Communication, Cloud and Big Data Analytics: Proceedings of CCB 2020; Springer: Singapore, 2022; pp. 27–37.
Qiu, R.; Qin, Y.; Li, Y.; Zhou, X.; Fu, J.; Li, W.; Shi, J. A software-defined security middle platform architecture. In Proceedings of the 5th International Conference on Computer Science and Software Engineering, Guilin, China, 21–23 October 2022; pp. 647–651.
W. Rafique, L. Qi, I. Yaqoob, M. Imran, R. U. Rasool, and W. Dou, “Complementing IoT services through software-defined networking and edge computing: a comprehensive survey,” IEEE Communications Surveys & Tutorials., vol. 22, no. 3, pp. 1761–1804, 2020.
P. Sivakumar, “Improved Resource management and utilization based on a fog-cloud computing system with IoT incorporated with Classifier systems,” Microprocessors and Microsystems, 2021.
S. Khan, M. Ali, N. Sher, Y. Asim, W. Naeem, and M. Kamran, “Software-defined networks (SDNs) and internet of things (IoTs): a qualitative prediction for 2020,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 11, 2016.
Restuccia, F., D’Oro, S., & Melodia, T. (2018). Securing the internet of things in the age of machine learning and software-defined networking. IEEE Internet of Things Journal, 5(6), 4829-4842.
Dai, M., Su, Z., Li, R., & Yu, S. (2021). A Software-defined-networking-enabled approach for edge-cloud computing in the Internet of Things. IEEE Network, 35(5), 66-73.
Bahashwan, A. A., Anbar, M., Manickam, S., Al-Amiedy, T. A., Aladaileh, M. A., & Hasbullah, I. H. (2023). A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking. Sensors, 23(9), 4441.
Shukla, P. K., Maheshwary, P., Subramanian, E. K., Shilpa, V. J., & Varma, P. R. K. (2023). Traffic flow monitoring in software-defined network using modified recursive learning. Physical Communication, 57, 101997.
Negera, W. G., Schwenker, F., Debelee, T. G., Melaku, H. M., & Feyisa, D. W. (2023). Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT. Applied Sciences, 13(8), 4699.
Wang, P., Yang, L. T., Nie, X., Ren, Z., Li, J., & Kuang, L. (2020). Data-driven software defined network attack detection: State-of-the-art and perspectives. Information Sciences, 513, 65-83.
Assis, M. V., Carvalho, L. F., Lloret, J., & Proença Jr, M. L. (2021). A GRU deep learning system against attacks in software defined networks. Journal of Network and Computer Applications, 177, 102942.
Mohammed, S. S., Hussain, R., Senko, O., Bimaganbetov, B., Lee, J., Hussain, F. ... & Bhuiyan, M. Z. A. (2018, October). A new machine learning-based collaborative DDoS mitigation mechanism in software-defined network. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1-8). IEEE.
Alajdah, A. H. I. (2022). A new software defined network (SDN) in IoTs based deep learning techniques (Master's thesis, Altınbaş Üniversitesi/Lisansüstü Eğitim Enstitüsü).
Saqib, M., Khan, F. Z., Ahmed, M., & Mehmood, R. M. (2019). A critical review on security approaches to software-defined wireless sensor networking. International Journal of Distributed Sensor Networks, 15(12), 1550147719889906
X. Li, L. Ji, H. Zhu, P. Li, X. Jia, and C. Li, “Cellular automata-based simulation of cross-space transmission of energy local area network risks: a case study of a power supply station in Beijing,” Sustainable Energy, Grids and Networks, vol. 27, Article ID 100521, 2021.
L. Leenen and T. Meyer, “Artificial intelligence and big data analytics in support of cyber defines,” In Research Anthology on Artificial Intelligence Applications in Security 2021, IGI Global, pp. 1738–1753.
T. P. Latchoumi and L. Parthiban, “Quasi oppositional dragonfly algorithm for load balancing in cloud computing environment,” 2021.
Bicaku, M. Tauber, and J. Delsing, “Security standard compliance and continuous verification for industrial internet of things,” International Journal of Distributed Sensor Networks, vol. 16, no. 6, Article ID 155014772092273, 2020.
Nižetić, P. Šolić, D. López-de-Ipiña González-de-Artaza, and L. Patrono, “Internet of Things (IoT): opportunities, issues and challenges towards a smart and sustainable future,” Journal of Cleaner Production, vol. 274, Article ID 122877, 2020.
M. Razian, M. Fathian, and R. Buyya, “ARC: anomaly-aware Robust Cloud-integrated IoT service composition based on uncertainty in advertised quality of service values,” Journal of Systems and Software, vol. 164, Article ID 110557, 2020.
W. T. Vambe, C. Chang, and K. Sibanda, “A review of quality of service in fog computing for the Internet of Things,” International Journal of Flow Control, vol. 3, no. 1, pp. 22–40, 2020.
J. Ramakrishnan, M. S. Shabbir, N. M. Kassim, P. T. Nguyen, and D. Mavaluru, “A comprehensive and systematic review of the network virtualization techniques in the IoT,” International Journal of Communication Systems, vol. 33, no. 7, Article ID e4331, 2020.
S. Zroug, I. Remadna, L. Kahloul, S. Benharzallah, and S. L. Terrissa, “Leveraging the power of machine learning for performance evaluation prediction in wireless sensor networks,” in Proceedings of the 2021 International Conference on Information Technology (ICIT), pp. 864–869, IEEE, Amman, Jordan, 2021 Jul 14.
Y. Xiao, G. Niu, L. Xiao, Y. Ding, S. Liu, and Y. Fan, “Reinforcement learning based energy-efficient internet-of-things video transmission,” Intelligent and Converged Networks, vol. 1, no. 3, pp. 258–270, 2020.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
