Network virtualization is a crucial facilitator for the swift advancement of 5G networks by providing flexibility, scalability, and optimal resource allocation. Although network function virtualization and network slicing offer on-demand services for various tenants, the difficulty of developing a resilient security architecture in 5G persists. This paper presents a distributed, multi-layered security infrastructure augmented by an AI-driven anomaly detection module to overcome this gap. The framework utilizes various virtual security functions (VSFs) throughout the layers of the 5G architecture, with the AI module facilitating real-time identification of anomalous traffic patterns and zero-day vulnerabilities. This modular design allocates security tasks among VSFs, activating them only as necessary, hence assuring efficiency and resilience. Simulation outcomes indicate that the suggested method markedly enhances detection precision and security resilience while minimizing processing overhead. Moreover, in comparison to traditional centralized approaches, the improved framework demonstrates higher performance for load balancing, coverage, distance, throughput, and attack detection efficacy.
AbdulhamidA.KabirS.GhafirI.LeiC. (2023). An overview of safety and security analysis frameworks for the internet of things. Electronics (Basel), 12(14), 3086. https://doi.org/10.3390/electronics12143086
2.
AbidiM. H.AlkhalefahH.MoiduddinK.AlazabM.MohammedM. K.AmeenW.GadekalluT. R. (2021). Optimal 5G network slicing using machine learning and deep learning concepts. Computer Standards & Interfaces, 76, 103518. https://doi.org/10.1016/j.csi.2021.103518
3.
AfolabiI.TalebT.SamdanisK.KsentiniA.FlinckH. (2018). Network slicing and softwarization: A survey on principles, enabling technologies, and solutions. IEEE Communications Surveys & Tutorials, 20(3), 2429–2453. https://doi.org/10.1109/COMST.2018.2815638
4.
AhmedS. F.Bin AlamM. S.HoqueM.LameesaA.AfrinS.FarahT.KabirM.ShafiullahG. M.MuyeenS. M. (2023). Industrial Internet of Things enabled technologies, challenges, and future directions. Computers and Electrical Engineering, 110, 108847. https://doi.org/10.1016/j.compeleceng.2023.108847
5.
Al BarazanchiI. I.HashimW. (2023). Enhancing IoT device security through blockchain technology: A decentralized approach. SHIFRA, 2023, 10–16. https://doi.org/10.70470/SHIFRA/2023/002
6.
AlqudahA.MohaidatM.AltawilI. (2013). Control of variable speed drive (VSD) based on diode clamped multilevel inverter using direct torque control and fuzzy logic. In: 2013 IEEE Jordan conference on applied electrical engineering and computing technologies (aEECT) (pp. 1–6). IEEE.
7.
Benlloch-CaballeroP.WangQ.CaleroJ. M. A. (2023). Distributed dual-layer autonomous closed loops for self-protection of 5G/6G IoT networks from distributed denial of service attacks. Computer Networks, 222, 109526. https://doi.org/10.1016/j.comnet.2022.109526
8.
BoualouacheA.BrikB.SenouciS.-M.EngelT. (2023). On-demand security framework for 5GB vehicular networks. IEEE Internet of Things Magazine, 6(2), 26–31. https://doi.org/10.1109/IOTM.001.2200233
9.
CaoY.Al-BarakatiA. (2025). Adaptive neural event-triggered secure control for nonlinear MASs against actuator faults and FDI attacks. International Journal of Systems Science, 1–14.
10.
ChopraG.JhaR. K.JainS. (2019). Rba: Region based algorithm for secure harvesting in ultra dense network. Journal of Network and Computer Applications, 125, 179–189. https://doi.org/10.1016/j.jnca.2018.09.020
11.
CookJ.RehmanS. U.KhanM. A. (2023). Security and privacy for low power iot devices on 5g and beyond networks: Challenges and future directions. IEEE Access, 11, 39295–39317. https://doi.org/10.1109/ACCESS.2023.3268064
12.
DasD.BanerjeeS.DasguptaK.ChatterjeeP.GhoshU.BiswasU. (2023). Blockchain enabled sdn framework for security management in 5g applications. In: Proceedings of the 24th international conference on distributed computing and networking (pp. 414–419).
13.
FurstenauL. B.RodriguesY. P. R.SottM. K.LeivasP.DohanM. S.López-RoblesJ. R.CoboM. J.BragazziN. L.ChooK.-K. R. (2023). Internet of things: Conceptual network structure, main challenges and future directions. Digital Communications and Networks, 9(3), 677–687. https://doi.org/10.1016/j.dcan.2022.04.027
14.
GuanJ.WeiZ.YouI. (2018). GRBC-based network security functions placement scheme in SDS for 5G security. Journal of Network and Computer Applications, 114, 48–56. https://doi.org/10.1016/j.jnca.2018.03.013
15.
HosseiniS. (2022). Evaluation of splint effect on the dimensional variations of implants location transfer with a 25˚ angle by open tray molding method. Nveo-Natural Volatiles & Essential Oils Journal NVEO, 9(1), 1122–1145.
16.
HuangZ (2025). A Unified Adaptive Event‐Triggered Output Feedback Consensus for Multi‐Agent Systems With or Without Output Constraints. Food Science & Nutrition, 35(4), 1390–1405.
17.
LilhoreU. K.DalalS.SimaiyaS. (2024). A cognitive security framework for detecting intrusions in IoT and 5G utilizing deep learning. Computers & Security, 136, 103560. https://doi.org/10.1016/j.cose.2023.103560
18.
LinC.-C.TsaiC.-T.LiuY.-L.ChangT.-T.ChangY.-S. (2023). Security and privacy in 5g-iiot smart factories: Novel approaches, trends, and challenges. Mobile Networks and Applications, 28(3), 1043–1058. https://doi.org/10.1007/s11036-023-02143-5
19.
LiuS. (2025). A novel event-triggered mechanism-based optimal safe control for nonlinear multi-player systems using adaptive dynamic programming. Journal of the Franklin Institute, 362(11): 107761.
20.
LiuS.XuN.LIL.AlharbiK. (2025). Zero-sum games-based optimal fault tolerant control for control-constrained multiplayer systems with external disturbances via adaptive dynamic programming. Communications in Nonlinear Science and Numerical Simulation, 2025, 25–42.
21.
LiuY.SunY.FuC.WenQ. (2022). Research on end-to-end bearer scheme for precise control of transmission network based on 5g communication technology. In: 2022 3rd international conference on computer vision, image and deep learning & international conference on computer engineering and applications (CVIDL & ICCEA) (pp. 499–501), IEEE.
22.
NieG.RezvaniE. (2025). Towards an efficient scheduling strategy based on multi-objective optimization in fog environments. Computing, 107.
23.
PandiS.AlbertA. J.ThapaK. N. K.KrishnaprasannaR. (2024). A novel enhanced security architecture for sixth generation (6G) cellular networks using authentication and acknowledgement (AA) approach. Results in Engineering, 21, 101669. https://doi.org/10.1016/j.rineng.2023.101669
24.
RezaeeA.SheikhabadO. A.BeygiL. (2021). Quality of transmission-aware control plane performance analysis for elastic optical networks. Computer Networks, 187, 107755. https://doi.org/10.1016/j.comnet.2020.107755
25.
SalahdineF.HanT.ZhangN. (2023). Security in 5G and beyond recent advances and future challenges. Security and Privacy, 6(1), e271. https://doi.org/10.1002/spy2.271
26.
SefatiS. S.HalungaS. (2023). Ultra-reliability and low-latency communications on the internet of things based on 5G network: Literature review, classification, and future research view. Transactions on Emerging Telecommunications Technologies, 34(6), e4770. https://doi.org/10.1002/ett.4770
27.
SharmaV.YouI.LeuF.-Y.AtiquzzamanM. (2018). Secure and efficient protocol for fast handover in 5G mobile Xhaul networks. Journal of Network and Computer Applications, 102, 38–57. https://doi.org/10.1016/j.jnca.2017.11.004
28.
SkA.MasilamaniV. (2018). A novel digital watermarking scheme for data authentication and copyright protection in 5G networks. Computers & Electrical Engineering, 72, 589–605. https://doi.org/10.1016/j.compeleceng.2018.02.045
29.
SunJ.ZhangY.TrikM. (2024). PBPHS: A profile-based predictive handover strategy for 5G networks. Cybernetics and Systems, 55(5), 1041–1062. https://doi.org/10.1080/01969722.2022.2129375
30.
TavoliR.RezvaniE.Hosseini ShirvaniM. (2025). An efficient hybrid approach based on deep learning and stacking ensemble using the whale optimization algorithm for detecting attacks in IoT devices. Engineering Reports, 7(9), e70338.
31.
WangG.WuJ.TrikM. (2023). A novel approach to reduce video traffic based on understanding user demand and D2D communication in 5G networks. IETE Journal of Research, 70(6), 1–17. https://doi.org/10.1080/03772063.2023.2278696
32.
WangZ.JinZ.YangZ.ZhaoW.TrikM. (2023). Increasing efficiency for routing in internet of things using binary gray wolf optimization and fuzzy logic. Journal of King Saud University-Computer and Information Sciences, 35(9), 101732. https://doi.org/10.1016/j.jksuci.2023.101732
WuX.DingS.ZhaoN.WangH.NiuB. (2025). Neural-network-based event-triggered adaptive secure fault-tolerant containment control for nonlinear multi-agent systems under denial-of-service attacks. Neural Networks, 9(191). https://doi.org/10.1016/j.neunet.2025.107725
35.
WuX.ZongGWangH.ZhaoX (2025). Collision-free distributed adaptive resilient formation control for underactuated USVs subject to intermittent actuator faults and denial-of-service attacks. IEEE Transactions on Vehicular Technology.
36.
WuY.NiuB.LiL.ZhaoX.AlotaibiN. (2025). Switching event‐triggered adaptive finite‐time control for nonlinear networked systems. Asian Journal of Control, 24, 21–35.
37.
XuH.ZhaoN.XuN.NiuB.ZhaoX. (2025). Reinforcement learning-based dynamic event-triggered prescribed performance control for nonlinear systems with input delay. International Journal of Systems Science, 1–16.
38.
YangJ.RezvaniE. (2025). QB-AutoIDS: A blockchain-based decentralized autonomous cyberattack detection system for consumer electronics using hybrid double Q-learning and Bi-LSTM. IEEE Transactions on Consumer Electronics, 6(2), 251–265.
39.
ZhangW.XuN.ZhaoN.Al-BarakatiA. (2025). Adaptive neural finite-time self-triggered control for nonstrict-feedback nonlinear systems with sensor faults. Robotic Intelligence and Automation, 45(3), 373–385.
40.
ZhangW.ZongG.NiuB. (2025). Adaptive fuzzy dynamic event-triggered control for PDE-ODE cascaded systems with actuator failures. Fuzzy Sets and Systems, 2(1), 109514.
41.
ZhangY.LiJ.ZhengD.LiP.TianY. (2018). Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice. Journal of Network and Computer Applications, 122, 50–60. https://doi.org/10.1016/j.jnca.2018.07.017
42.
ZhangZ.ZhaoN.AhmadA. (2025). Observer-based adaptive secure control for networked switched nonlinear systems under DoS attacks. International Journal of General Systems, 1, 35.
43.
ZhangZ.ZhaoX. (2025). Observer-assisted model free adaptive predictive control for distributed heterogeneous multi-agent systems against DoS attacks. IEEE Internet of Things Journal., 12(6), 124–142.
44.
ZhaoJ.KarimiH.ZhaoX. (2025). Safe optimal control for multiplayer Stackelberg–Nash games of nonlinear time-delay systems via adaptive dynamic programming. Neurocomputing, 6(145), 1142–1160131203.
