An Efficient Security Framework for 5G DDoS Attack using Machine Learning and Deep Learning

doi:10.23940/ijpe.25.06.p3.316325

Abstract

Abstract: Assaults known as Distributed Denial-of-Service (DDoS) overload target servers, services, or networks with malicious data, making them unavailable to authorized users. These assaults are becoming a bigger problem, impacting governments, corporations, and web applications. DDoS assaults are predicted to become far more frequent and sophisticated when 5G and other future network technologies are introduced and expanded. An improved, effective, and secure detection system is therefore desperately needed to safeguard vital network equipment and enable the smooth rollout of 5G networks. A novel DDoS detection system that combines a composite multilayer perceptron (MLP) with an efficient feature extraction approach is developed in order to meet this difficulty. The methodology distinguishes between harmful and benign activity using a dataset created from simulated network traffic. Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) algorithms were used to examine network patterns in order to classify the data. Furthermore, the system uses two different Deep Neural Networks (DNN1 and DNN2), each of which has a special architecture intended to improve the detection process’s accuracy and resilience. Comparing the suggested approach to conventional classifiers reveals remarkable performance. According to experimental data, the framework achieves a remarkable accuracy rate of 99.71% and a wonderfully low loss of 0.011. This performance demonstrates the promise of deep learning approaches to improve security of networks, since it greatly outperforms SVM and KNN classifiers. The created DDoS detection solution guarantees safe, effective, and dependable 5G network operations without creating new vulnerabilities in addition to providing high accuracy and little performance loss. In the age of sophisticated wireless communication networks, this framework is an essential first step in reducing DDoS threats.

Key words: network security, DDoS detection, multilayer perceptron (MLP), 5G networks, feature extraction, deep neural networks (DNN)

Sharma Ji and Abhishek Kumar Mishra. An Efficient Security Framework for 5G DDoS Attack using Machine Learning and Deep Learning [J]. Int J Performability Eng, 2025, 21(6): 316-325.

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References

[1] Forland M.K., Kralevska K., Garau M., andGligoroski D., 2019. Preventing DDoS with SDN in 5G. In2019 IEEE Globecom Workshops (GC Workshops), pp. 1-7.
[2] Amaizu G.C., Nwakanma C.I., Bhardwaj S., Lee J.M., andKim D.S., 2021. Composite and efficient DDoS attack detection framework for B5G networks.Computer Networks, 188, 107871.
[3] Arshi M., Nasreen M.D., andMadhavi K., 2020. A survey of DDoS attacks using machine learning techniques. InE3S Web of Conferences, 184, pp. 01052.
[4] Radanliev P., De Roure D.C., Nicolescu R., Huth M., Montalvo R.M., Cannady S., andBurnap P., 2018. Future developments in cyber risk assessment for the internet of things.Computers in Industry, 102, pp. 14-22.
[5] Mamolar A.S., Pervez Z., Wang Q., andAlcaraz-Calero J.M., 2019. Towards the detection of mobile DDoS attacks in 5g multi-tenant networks. In2019 European Conference on Networks and Communications (EuCNC), pp. 273-277.
[6] Zhang C., Ueng Y.L., Studer C., andBurg A., 2020. Artificial intelligence for 5G and beyond 5G: implementations, algorithms, and optimizations. IEEE Journal on Emerging and Selected Topics in Circuits and Systems,10(2), pp. 145-148.
[7] Hong K., Kim Y., Choi H., andPark J., 2017. SDN-assisted slow HTTP DDoS attack defense method. IEEE Communications Letters,22(4), pp. 688-691.
[8] Abou El Houda Z., Hafid A.S., andKhoukhi L., 2019. Cochain-SC: an intra-and inter-domain DDoS mitigation scheme based on blockchain using SDN and smart contract.IEEE Access, 7, pp. 98893-98907.
[9] Zhou L., Sood K., andXiang Y., 2019. ERM: an accurate approach to detect DDoS attacks using entropy rate measurement. IEEE Communications Letters,23(10), pp. 1700-1703.
[10] Somani G., Gaur M.S., Sanghi D., Conti M., andRajarajan M., 2017. Scale inside-out: rapid mitigation of cloud DDoS attacks. IEEE Transactions on Dependable and Secure Computing,15(6), pp. 959-973.
[11] Lilhore U.K., Dalal S., andSimaiya S., 2024. A cognitive security framework for detecting intrusions in IoT and 5G utilizing deep learning.Computers & Security, 136, 103560.
[12] Ji S., andMishra A.K., 2024. 5g security issues challenges and solutions against DDoS attacks: A survey. In2024 2nd International Conference on Disruptive Technologies (ICDT), pp. 1422-1427.
[13] Ji S., andMishra A.K., 2024. 5g network implementation: A survey on security issues, challenges, and future directions. InDevelopments Towards Next Generation Intelligent Systems for Sustainable Development, pp. 62-88.
[14] Musa M.O., andOdokuma E.E., 2023. A framework for the detection of distributed denial of service attacks on network logs using ML and DL classifiers. Scientia Africana,22(3), pp. 153-164.
[15] Yan Q., Huang W., Luo X., Gong Q., andYu F.R., 2018. A multi-level DDoS mitigation framework for the industrial internet of things. IEEE Communications Magazine,56(2), pp. 30-36.
[16] Almiani M., AbuGhazleh A., Jararweh Y., andRazaque A., 2021. DDoS detection in 5G-enabled IoT networks using deep kalman backpropagation neural network. International Journal of Machine Learning and Cybernetics,12(11), pp. 3337-3349.
[17] Kim Y.E., Kim Y.S., andKim H., 2022. Effective feature selection methods to detect IoT DDoS attack in 5G core network.Sensors, 22(10), 3819.
[18] Sharafaldin I., Lashkari A.H., Hakak S., andGhorbani A.A., 2019. Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In2019 International Carnahan Conference on Security Technology (ICCST), pp. 1-8.
[19] Yuan X., Li C., andLi X., 2017. DeepDefense: identifying DDoS attack via deep learning. In2017 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1-8.