Enhancing IoT Security in 5G Networks: Mitigating DDoS Attacks With Deep Learning

L. Chettri and R. Bera, "A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems," IEEE Internet of Things Journal, vol. 7, no. 1, pp. 16-32, Jan. 2020, doi: 10.1109/JIOT.2019.2948888.

J. Ding, M. Nemati, C. Ranaweera, and J. Choi, "IoT Connectivity Technologies and Applications: A Survey," IEEE Access, vol. 8, pp. 67646-67673, 2020, doi: 10.1109/ACCESS.2020.2985932.

S. I. Al-Sharekh and K. H. A. Al-Shqeerat, "An Overview of Privacy Issues in IoT Environments," 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), Al Madinah Al Munawwarah, Saudi Arabia, 2020, pp. 1-6, doi: 10.1109/AECT47998.2020.9194197.

L. Tawalbeh, F. Muheidat, M. Tawalbeh, and M. Quwaider, "IoT Privacy and Security: Challenges and Solutions," Applied Sciences, vol. 10, no. 12, p. 4102, Jun. 2020, doi: 10.3390/app10124102.

M. Pons, E. Valenzuela, B. Rodríguez, J. A. Nolazco-Flores, and C. Del-Valle-Soto, "Utilization of 5G Technologies in IoT Applications: Current Limitations by Interference and Network Optimization Difficulties—A Review," Sensors, vol. 23, no. 8, p. 3876, 2023, doi: 10.3390/s23083876.

A. Kumari, D. Gupta, and M. Uppal, "Unifying RNN and KNN for Enhancing Mirai Attack Detection in IoT Networks," 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), Bangalore, India, 2024, pp. 1-5, doi: 10.1109/ICITEICS61368.2024.10625616.

M. Adam, M. Hammoudeh, R. Alrawashdeh, and B. Alsulaimy, "A Survey on Security, Privacy, Trust, and Architectural Challenges in IoT Systems," IEEE Access, vol. 12, pp. 57128-57149, 2024, doi: 10.1109/ACCESS.2024.3382709.

R. Doriguzzi-Corin, S. Millar, S. Scott-Hayward, J. Martinez-del-Rincon, and D. Siracusa, "Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection," IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 876–889, Jun. 2020, doi: 10.1109/tnsm.2020.2971776.

R. Vishwakarma and A. K. Jain, "A survey of DDoS attacking techniques and defence mechanisms in the IoT network," Telecommunication Systems, Jul. 2019, doi: 10.1007/s11235-019-00599-z.

K. Kaur and J. Ayoade, "Analysis of DDoS Attacks on IoT Architecture," 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Palembang, Indonesia, 2023, pp. 332-337, doi: 10.1109/EECSI59885.2023.10295766.

S. Dong, P. Wang, and K. Abbas, "A survey on Deep Learning and its applications," Computer Science Review, vol. 40, p. 100379, May 2021, doi: 10.1016/j.cosrev.2021.100379.

S. Dargan, M. Kumar, M. R. Ayyagari, and G. Kumar, "A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning," Archives of Computational Methods in Engineering, vol. 27, Jun. 2019, doi: 10.1007/s11831-019-09344-w.

W. Zhang, H. Li, Y. Li, H. Liu, Y. Chen, and X. Ding, "Application of Deep Learning algorithms in geotechnical engineering: a short critical review," Artificial Intelligence Review, vol. 54, no. 8, pp. 5633–5673, Feb. 2021, doi: 10.1007/s10462-021-09967-1.

T. Khempetch and P. Wuttidittachotti, "DDoS attack detection using deep learning," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 2, p. 382, Jun. 2021, doi: 10.11591/ijai.v10.i2.pp382-388.

A. Sherstinsky, "Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network," Physica D: Nonlinear Phenomena, vol. 404, p. 132306, Mar. 2020, doi: 10.1016/j.physd.2019.132306.

J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, "A comprehensive survey on support vector machine classification: Applications, challenges and trends," Neurocomputing, vol. 408, no. 1, pp. 189–215, Sep. 2020, doi: 10.1016/j.neucom.2019.10.118.

I. H. Sarker, "Machine Learning: Algorithms, Real-World Applications and Research Directions," SN Computer Science, vol. 2, no. 3, pp. 1–21, Mar. 2021, doi: 10.1007/s42979-021-00592-x.

S. Messaoud, A. Bradai, S. H. R. Bukhari, P. T. A. Quang, O. B. Ahmed, and M. Atri, "A survey on machine learning in Internet of Things: Algorithms, strategies, and applications," Internet of Things, vol. 12, p. 100314, Dec. 2020, doi: 10.1016/j.iot.2020.100314.

L. Ma, Y. Chai, L. Cui, D. Ma, Y. Fu, and A. Xiao, "A Deep Learning-Based DDoS Detection Framework for Internet of Things," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148944.

F. Hussain, S. G. Abbas, M. Husnain, U. U. Fayyaz, F. Shahzad, and G. A. Shah, "IoT DoS and DDoS Attack Detection using ResNet," 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 2020, pp. 1-6, doi: 10.1109/INMIC50486.2020.9318216.

G. C. Amaizu et al., "Composite and Efficient DDoS Attack Detection Framework for B5G Networks," Computer Networks, vol. 188, p. 107871, Apr. 2021, doi: 10.1016/j.comnet.2021.107871.

I. A. Alnuman and M. Al-Akhras, "Machine Learning DDoS Detection for Generated Internet of Things Dataset (IoT Dat)," 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2020, pp. 1-6, doi: 10.1109/ICCIS49240.2020.9257714.

A. S. Alqahtani, "FSO-LSTM IDS: Hybrid Optimized and Ensembled Deep-Learning Network-Based Intrusion Detection System for Smart Networks," The Journal of Supercomputing, Jan. 2022, doi: 10.1007/s11227-021-04285-3.

S. Bishnoi, S. Mohanty, and B. Sahoo, "A Deep Learning-Based Methodology in Fog Environment for DDoS Attack Detection," 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2021, pp. 201-206, doi: 10.1109/ICCMC51019.2021.9418363.

P. A. B. Bautista, L. F. Urquiza-Aguiar, L. L. Cárdenas, and M. A. Igartua, "Large-Scale Simulations Manager Tool for OMNeT++: Expediting Simulations and Post-Processing Analysis," IEEE Access, vol. 8, pp. 159291-159306, 2020, doi: 10.1109/ACCESS.2020.3020745.

G. Nardini, D. Sabella, G. Stea, P. Thakkar, and A. Virdis, "Simu5G–An OMNeT++ Library for End-to-End Performance Evaluation of 5G Networks," IEEE Access, vol. 8, pp. 181176-181191, 2020, doi: 10.1109/ACCESS.2020.3028550.

CANONICAL Organization, "Download Ubuntu desktop," [Online]. Available: https://ubuntu.com/download/desktop. [Accessed: Jan. 27, 2023].

OMNeT++ Organization, "OMNeT++," [Online]. Available: https://omnetpp.org/. [Accessed: Jan. 15, 2023].

OMNeT++ Organization, "Simu5G," [Online]. Available: https://omnetpp.org/download-items/simu5g.html. [Accessed: Jan. 15, 2023].

OMNeT++ Organization, "INET Framework," [Online]. Available: https://omnetpp.org/download-items/inet.html. [Accessed: Jan. 15, 2023].

A. Varga and R. Hornig, "An Overview of the OMNeT++ Simulation Environment," Proceedings of the First International ICST Conference on Simulation Tools and Techniques for Communications Networks and Systems, 2008, doi: 10.4108/icst.simutools2008.3027.

U. Kingdom, "What is a GNB (gNodeB)?," Inseego.com, [Online]. Available: https://inseego.com/uk/resources/5g-glossary/what-is-gnb/. [Accessed: Nov. 19, 2024].

M. DeNapoli, "Get to Know 5G – Part 1," Cisco Blogs, Nov. 22, 2021. [Online]. Available: https://blogs.cisco.com/developer/gettoknow5g0. [Accessed: Jan. 27, 2023].

P. Salva-Garcia, J. M. Alcaraz-Calero, Q. Wang, J. B. Bernabe, and A. Skarmeta, "5G NB-IoT: Efficient Network Traffic Filtering for Multitenant IoT Cellular Networks," Security and Communication Networks, vol. 2018, pp. 1–21, Dec. 2018, doi: 10.1155/2018/9291506.

R. M. Alzhrani and M. Alliheedi, "5G Networks and IoT Devices: Mitigating DDoS Attacks with Deep Learning Techniques," arXiv (Cornell University), Nov. 2023, doi: 10.48550/arxiv.2311.06938.

C. Shannon, D. Moore, and K. C. Claffy, "Beyond Folklore: Observations on Fragmented Traffic," IEEE/ACM Transactions on Networking, vol. 10, no. 6, pp. 709-720, Dec. 2002, doi: 10.1109/TNET.2002.805028.

A. E. Cil, K. Yildiz, and A. Buldu, "Detection of DDoS Attacks with Feed Forward-Based Deep Neural Network Model," Expert Systems with Applications, vol. 169, p. 114520, May 2021, doi: 10.1016/j.eswa.2020.114520.

S. Zhang, "Challenges in KNN Classification," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 10, pp. 1–1, 2021, doi: 10.1109/TKDE.2021.3049250.

W. Dudzik, J. Nalepa, and M. Kawulok, "Evolving Data-Adaptive Support Vector Machines for Binary Classification," Knowledge-Based Systems, vol. 227, p. 107221, Sep. 2021, doi: 10.1016/j.knosys.2021.107221.

S. Kiranyaz, O. Avci, O. Abdeljaber, M. Gabbouj, and D. Inman, "1D Convolutional Neural Networks and Applications: A Survey," Mechanical Systems and Signal Processing, vol. 151, p. 107398, Apr. 2021, doi: 10.1016/j.ymssp.2020.107398..