Navigating Cyber Threats
The Role of Machine Learning and Deep Learning in Fifth-Generation Internet of Things Security
DOI:
https://doi.org/10.14500/aro.12365Keywords:
5G, Attacks, Deep learning, Internet of things, Machine learningAbstract
The high-speed development of Fifth Generation technologies announces a new era for the internet of things (IoT), distinguished by high-rate connectivity, speed, and low latency. However, this advancement also opens doors to major security challenges and expands the attack surface. Existing general IoT surveys do not systematically analyze Fifth Generation-enabled IoT concerns, which creates a clear need for a focused synthesis of machine learning and deep learning (DL) defenses tailored to Fifth Generation-IoT constraints and threat models. To address this gap, this survey conducts a preferred reporting items for systematic reviews and meta-analyses-guided analysis of recent studies published in the past 5 years, extracting methodologies, results, datasets, metrics, tools, and reported limitations to answer explicit research questions about which approaches work, under which conditions, and with what deployment implications for Fifth Generation IoT threat detection and mitigation. The results show that DL families and hybrid deep models dominate intrusion, anomaly, and malicious traffic detection, while research overemphasizes denial-of-service attacks relative to Replay, Ransomware, Sybil, Man-in-the-Middle, and Phishing attacks. The recommendations, which come from comparative evidence across datasets, attack categories, and model performance limitations, emphasize the need for more diverse and realistic Fifth Generation IoT datasets as well as for understudied learning paradigms, such as continual learning, federated learning, meta-learning, and self-supervised learning. These insights highlight clear research directions toward adaptive, privacy-preserving, and generalizable intrusion detection in Fifth Generation-IoT systems.
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References
AHAD, A., TAHIR, M., AMAN SHEIKH, M., AHMED, K. I., MUGHEES, A. & NUMANI, A. 2020. Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review. Sensors, 20, 4047, doi: https://doi.org/10.3390/s20144047.
ALFAW, A. H. & AL-OMARY, A. 5G Security Threats. 2022 International Conference on Data Analytics for Business and Industry (ICDABI), 25-26 Oct. 2022 2022. 196-199, doi: https://doi.org/10.1109/ICDABI56818.2022.10041502.
ALQURA’N, R., ALJAMAL, M., AL-AIASH, I., ALSARHAN, A., KHASSAWNEH, B., ALJAIDI, M. & ALANAZI, R. 2024. Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach. IoT, 5, 478-508, doi: https://doi.org/10.3390/iot5030022.
ALSARIERA, Y. A., AWWAD, W. F., ALGARNI, A. D., ELMANNAI, H., GAMARRA, M. & ESCORCIA-GUTIERREZ, J. 2024. Enhanced Dwarf Mongoose optimization algorithm with deep learning-based attack detection for drones. Alexandria Engineering Journal, 93, 59-66, doi: https://doi.org/10.1016/j.aej.2024.02.048.
ALSHEHRI, M. S., AHMAD, J., ALMAKDI, S., QATHRADY, M. A., GHADI, Y. Y. & BUCHANAN, W. J. 2024. SkipGateNet: A Lightweight CNN-LSTM Hybrid Model With Learnable Skip Connections for Efficient Botnet Attack Detection in IoT. IEEE Access, 12, 35521-35538, doi: https://doi.org/10.1109/ACCESS.2024.3371992.
ANAND, A., RANI, S., ANAND, D., ALJAHDALI, H. M. & KERR, D. 2021. An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications. Sensors, 21, 6346.
AOUEILEYINE, M. O.-E., KARMOUS, N., BOUALLEGUE, R., YOUSSEF, N. & YAZIDI, A. Detecting and Mitigating MitM Attack on IoT Devices Using SDN. In: BAROLLI, L., ed. Advanced Information Networking and Applications, 2024// 2024 Cham. Springer Nature Switzerland, 320-330, doi: https://doi.org/10.1007/978-3-031-57942-4_31.
APWG. 2024. PHISHING ACTIVITY TRENDS REPORT, 1st Quarter 2024 [Online]. APWG. Available: https://docs.apwg.org/reports/apwg_trends_report_q1_2024.pdf?_gl=1*1bft75a*_ga*MTgzODM0NTYwMC4xNzIyMTYzNzQ0*_ga_55RF0RHXSR*MTcyMjE2Mzc0NC4xLjAuMTcyMjE2Mzc0NC4wLjAuMA. [Accessed 28 July, 2024 2024].
BAHALUL HAQUE, A. K. M., NAUSHEEN, T., AL MAHFUJ SHAAN, A. & MURAD, S. A. 2023. Security Attacks and Countermeasures in 5G Enabled Internet of Things. In: BHUSHAN, B., SHARMA, S. K., KUMAR, R. & PRIYADARSHINI, I. (eds.) 5G and Beyond. Singapore: Springer Nature Singapore, doi: 10.1007/978-981-99-3668-7_7
BARSHAN, A., MOHAMMADI, S. M. A., ABDOLLAHI, F., DAVARANI, R. Z. & ESMAEILI, S. 2024. Local detection of replay attacks and data anomalies on PMU measurements of smart power grids via tracking critical dynamic modes. International Journal of Electrical Power & Energy Systems, 159, 110038, doi: https://doi.org/10.1016/j.ijepes.2024.110038.
BHARATI, S. & PODDER, P. 2022. Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions. Security and Communication Networks, 2022, 8951961, doi: https://doi.org/10.1155/2022/8951961.
CHENG, S. M., HONG, B. K. & HUNG, C. F. 2022. Attack Detection and Mitigation in MEC-Enabled 5G Networks for AIoT. IEEE Internet of Things Magazine, 5, 76-81, doi: https://doi.org/10.1109/IOTM.001.2100144.
CHETTRI, L. & BERA, R. 2020. A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems. IEEE Internet of Things Journal, 7, 16-32, doi: https://doi.org/10.1109/JIOT.2019.2948888.
DANG, Y., BENZAÏD, C., YANG, B., TALEB, T. & SHEN, Y. 2022. Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs. IEEE Internet of Things Journal, 9, 25068-25085, doi: https://doi.org/10.1109/JIOT.2022.3195320.
DANGI, R., LALWANI, P., CHOUDHARY, G., YOU, I. & PAU, G. 2022. Study and Investigation on 5G Technology: A Systematic Review. Sensors, 22, 26.
DAS, A. K., ROY, S., BANDARA, E. & SHETTY, S. 2023. Securing Age-of-Information (AoI)-Enabled 5G Smart Warehouse Using Access Control Scheme. IEEE Internet of Things Journal, 10, 1358-1375, doi: https://doi.org/10.1109/JIOT.2022.3205245.
DHANAVANTHINI, P. & CHAKKRAVARTHY, S. S. 2023. Phish-armour: phishing detection using deep recurrent neural networks. Soft Computing, doi: https://doi.org/10.1007/s00500-023-07962-y.
EL-SOFANY, H., EL-SEOUD, S. A., KARAM, O. H. & BOUALLEGUE, B. 2024. Using machine learning algorithms to enhance IoT system security. Scientific Reports, 14, 12077, doi: https://doi.org/10.1038/s41598-024-62861-y.
FAN, Y., LI, Y., ZHAN, M., CUI, H. & ZHANG, Y. IoTDefender: A Federated Transfer Learning Intrusion Detection Framework for 5G IoT. 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE), 31 Dec.-1 Jan. 2021 2020. 88-95, doi: https://doi.org/10.1109/BigDataSE50710.2020.00020.
FERRAG, M. A., DEBBAH, M. & AL-HAWAWREH, M. Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks. 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), 1-4 May 2023 2023. 16-25, doi: https://doi.org/10.1109/CCGridW59191.2023.00018.
GOYAL, S., RAJAWAT, A. S., SOLANK, R. K., PATIL, D. & POTGANTWAR, A. 2024. A Trustable Security Solutions using XAI for 5G-Enabled UAV. Journal of Logistics, Informatics and Service Science, 11, 73-86, doi: https://doi.org/10.33168/JLISS.2024.0404.
GUO, Y. 2023. A review of Machine Learning-based zero-day attack detection: Challenges and future directions. Computer Communications, 198, 175-185, doi: https://doi.org/10.1016/j.comcom.2022.11.001.
HABIBI, O., CHEMMAKHA, M. & LAZAAR, M. 2023. Imbalanced tabular data modelization using CTGAN and machine learning to improve IoT Botnet attacks detection. Engineering Applications of Artificial Intelligence, 118, 105669, doi: https://doi.org/10.1016/j.engappai.2022.105669.
ISPAHANY, J., ISLAM, M. R., ISLAM, M. Z. & KHAN, M. A. 2024. Ransomware Detection Using Machine Learning: A Review, Research Limitations and Future Directions. IEEE Access, 12, 68785-68813, doi: https://doi.org/10.1109/ACCESS.2024.3397921.
JIANG, C., XU, H., HUANG, C. & HUANG, Q. 2022. An Adaptive Information Security System for 5G-Enabled Smart Grid Based on Artificial Neural Network and Case-Based Learning Algorithms. Frontiers in Computational Neuroscience, 16, doi: https://doi.org/10.3389/fncom.2022.872978.
JUNG, J. H., HONG, M. Y., CHOI, H. & YOON, J. W. 2024. An Analysis of GPS Spoofing Attack and Efficient Approach to Spoofing Detection in PX4. IEEE Access, 12, 46668-46677, doi: https://doi.org/10.1109/ACCESS.2024.3382543.
KIM, Y.-E., KIM, M.-G. & KIM, H. 2022a. Detecting IoT Botnet in 5G Core Network Using Machine Learning. Computers, Materials & Continua, 72, 4467--4488.
KIM, Y.-E., KIM, Y.-S. & KIM, H. 2022b. Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network. Sensors, 22, 3819, doi: https://doi.org/10.3390/s22103819.
KORBA, A. A., BOUALOUACHE, A., BRIK, B., RAHAL, R., GHAMRI-DOUDANE, Y. & SENOUCI, S. M. Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks. ICC 2023 - IEEE International Conference on Communications, 28 May-1 June 2023 2023. 1137-1142, doi: https://doi.org/10.1109/ICC45041.2023.10279368
KORBA, A. A., BOUALOUACHE, A. & GHAMRI-DOUDANE, Y. 2024. Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV. IEEE Transactions on Vehicular Technology, 1-16, doi: https://doi.org/10.1109/TVT.2024.3385916.
KUMAR, N., AJAY, S. & PUSHPNEEL, V. 2022. Impact of 5G on IOT Implemented Devices. International Journal of Computer Science and Mobile Computing, doi: https://doi.org/10.47760/ijcsmc.2022.v11i03.015.
KUMARI, P. & JAIN, A. K. 2024. Timely detection of DDoS attacks in IoT with dimensionality reduction. Cluster Computing, doi: https://doi.org/10.1007/s10586-024-04392-9.
LEHR, W., QUEDER, F. & HAUCAP, J. 2021. 5G: A new future for Mobile Network Operators, or not? Telecommunications Policy, 45, 102086, doi: https://doi.org/10.1016/j.telpol.2020.102086.
LI, W., WANG, N., JIAO, L. & ZENG, K. 2021. Physical Layer Spoofing Attack Detection in MmWave Massive MIMO 5G Networks. IEEE Access, 9, 60419-60432, doi: https://doi.org/10.1109/ACCESS.2021.3073115.
LUO, Y., CHEN, X., SUN, H., LI, X., GE, N., FENG, W. & LU, J. 2024. Securing 5G/6G IoT Using Transformer and Personalized Federated Learning: An Access-Side Distributed Malicious Traffic Detection Framework. IEEE Open Journal of the Communications Society, 5, 1325-1339, doi: https://doi.org/10.1109/OJCOMS.2024.3365976.
LV, Z., SINGH, A. K. & LI, J. 2021. Deep Learning for Security Problems in 5G Heterogeneous Networks. IEEE Network, 35, 67-73, doi: https://doi.org/10.1109/MNET.011.2000229.
MALIK, A., BHUSHAN, B., BHATIA KHAN, S., KASHYAP, R., CHAGANTI, R. & RAKESH, N. 2023. Security Attacks and Vulnerability Analysis in Mobile Wireless Networking. In: BHUSHAN, B., SHARMA, S. K., KUMAR, R. & PRIYADARSHINI, I. (eds.) 5G and Beyond. Singapore: Springer Nature Singapore, doi: 10.1007/978-981-99-3668-7_5
MAROOFI, S., KORCZYŃSKI, M., HÖLZEL, A. & DUDA, A. 2021. Adoption of Email Anti-Spoofing Schemes: A Large Scale Analysis. IEEE Transactions on Network and Service Management, 18, 3184-3196, doi: https://doi.org/10.1109/TNSM.2021.3065422.
MARTINEZ QUINTERO, J. C., ESTUPIÑAN CUESTA, E. P. & RAMIREZ LOPEZ, L. J. 2023. A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms. Results in Engineering, 19, 101243, doi: https://doi.org/10.1016/j.rineng.2023.101243.
NAHA, A., TEIXEIRA, A., AHLÉN, A. & DEY, S. 2023. Sequential Detection of Replay Attacks. IEEE Transactions on Automatic Control, 68, 1941-1948, doi: https://doi.org/10.1109/TAC.2022.3174004.
NAIK, S., THIPPESWAMY, P., RAGHAVAN, A., RAJGOPAL, M. & SUJITH, A. 2024. Efficient network management and security in 5G enabled internet of things using deep learning algorithms. 14, doi: http://dx.doi.org/10.11591/ijece.v14i1.pp1058-1070.
OGBODO, E. U., ABU-MAHFOUZ, A. M. & KURIEN, A. M. 2022. A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security. Sensors, 22, 6313.
PATEL, D. & SHAH, D. Combating ARP Spoofing: Detection and Analysis Techniques. 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), 28 Feb.-1 March 2024 2024. 543-547, doi: https://doi.org/10.23919/INDIACom61295.2024.10498305.
PRASAD, V. M. & BHARATHI, B. 2023. Security in 5G Networks: A Systematic Analysis of High-Speed Data Connections. International Journal on Recent and Innovation Trends in Computing and Communication, doi: https://doi.org/10.17762/ijritcc.v11i5.6608.
RACHAKONDA, L. P., SIDDULA, M. & SATHYA, V. 2024. A comprehensive study on IoT privacy and security challenges with focus on spectrum sharing in Next-Generation networks (5G/6G/beyond). High-Confidence Computing, 4, 100220, doi: https://doi.org/10.1016/j.hcc.2024.100220.
RAKHI, S. & SHOBHA, K. R. 2023. LCSS Based Sybil Attack Detection and Avoidance in Clustered Vehicular Networks. IEEE Access, 11, 75179-75190, doi: https://doi.org/10.1109/ACCESS.2023.3294469.
RAZAULLA, S., FACHKHA, C., MARKARIAN, C., GAWANMEH, A., MANSOOR, W., FUNG, B. C. M. & ASSI, C. 2023. The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions. IEEE Access, 11, 40698-40723, doi: https://doi.org/10.1109/ACCESS.2023.3268535.
SADHWANI, S., MATHUR, A., MUTHALAGU, R. & PAWAR, P. M. 2024. 5G-SIID: an intelligent hybrid DDoS intrusion detector for 5G IoT networks. International Journal of Machine Learning and Cybernetics, doi: https://doi.org/10.1007/s13042-024-02332-y
SAMEERA, N. & SHASHI, M. 2020. Deep transductive transfer learning framework for zero-day attack detection. ICT Express, 6, 361-367, doi: https://doi.org/10.1016/j.icte.2020.03.003.
SHAFIQUE, K., KHAWAJA, B. A., SABIR, F., QAZI, S. & MUSTAQIM, M. 2020. Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios. IEEE Access, 8, 23022-23040, doi: https://doi.org/10.1109/ACCESS.2020.2970118.
SOUSA, B., MAGAIA, N. & SILVA, S. 2023. An Intelligent Intrusion Detection System for 5G-Enabled Internet of Vehicles. Electronics, 12, 1757.
TAHER, M. A., TARIQ, M., BEHNAMFAR, M. & SARWAT, A. I. 2023. Analyzing Replay Attack Impact in DC Microgrid Consensus Control: Detection and Mitigation by Kalman-Filter-Based Observer. IEEE Access, 11, 121368-121378, doi: https://doi.org/10.1109/ACCESS.2023.3317799.
TALEB, H., KHAWAM, K., LAHOUD, S., HELOU, M. E. & MARTIN, S. 2022. Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks. IEEE Access, 10, 58212-58224, doi: https://doi.org/10.1109/ACCESS.2022.3177629.
TARANNUM, S., USHA, S. M. & AHAMMED, G. F. A. 2024. A comprehensive study of LPWAN, LoRaWAN for IoT: Background, related research, performance, potential challenges and proposed methodology. AIP Conference Proceedings, 3122, doi: https://doi.org/10.1063/5.0216057.
TRABELSI, Z., PARAMBIL, M. M. A., QAYYUM, T. & ALOMAR, B. Teaching DNS Spoofing Attack Using a Hands-on Cybersecurity Approach Based on Virtual Kali Linux Platform. 2024 IEEE Global Engineering Education Conference (EDUCON), 8-11 May 2024 2024. 1-8, doi: https://doi.org/10.1109/EDUCON60312.2024.10578851.
TULAY, H. B. & KOKSAL, C. E. 2024. Sybil Attack Detection Based on Signal Clustering in Vehicular Networks. IEEE Transactions on Machine Learning in Communications and Networking, 2, 753-765, doi: https://doi.org/10.1109/TMLCN.2024.3410208.
VALADARES, D. C. G., WILL, N. C., SOBRINHO, Á. Á. C. C., LIMA, A. C. D., MORAIS, I. S. & SANTOS, D. F. S. Security Challenges and Recommendations in 5G-IoT Scenarios. In: BAROLLI, L., ed. Advanced Information Networking and Applications, 20 March 2023 2023 Cham. Springer International Publishing, 558-573, doi: https://doi.org/10.1007/978-3-031-29056-5_48.
VERMA, P., BHAROT, N., BRESLIN, J. G., SHEA, D. O., VIDYARTHI, A. & GUPTA, D. 2024. Zero-Day Guardian: A Dual Model Enabled Federated Learning Framework for Handling Zero-Day Attacks in 5G Enabled IIoT. IEEE Transactions on Consumer Electronics, 70, 3856-3866, doi: https://doi.org/10.1109/TCE.2023.3335385.
VIANA, J., FARKHARI, H., CAMPOS, L. M., SEBASTIÃO, P., KOUTLIA, K., LAGÉN, S., BERNARDO, L. & DINIS, R. A Convolutional Attention Based Deep Learning Solution for 5G UAV Network Attack Recognition over Fading Channels and Interference. 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 26-29 Sept. 2022 2022. 1-5, doi: https://doi.org/10.1109/VTC2022-Fall57202.2022.10012726.
VIANA, J., FARKHARI, H., SEBASTIÃO, P., CAMPOS, L. M., KOUTLIA, K., BOJOVIC, B., LAGÉN, S. & DINIS, R. 2024. Deep Attention Recognition for Attack Identification in 5G UAV Scenarios: Novel Architecture and End-to-End Evaluation. IEEE Transactions on Vehicular Technology, 73, 131-146, doi: https://doi.org/10.1109/TVT.2023.3302814.
WANG, N., LI, W., ALIPOUR-FANID, A., JIAO, L., DABAGHCHIAN, M. & ZENG, K. 2021. Pilot Contamination Attack Detection for 5G MmWave Grant-Free IoT Networks. IEEE Transactions on Information Forensics and Security, 16, 658-670, doi: 10.1109/TIFS.2020.3017932.
WAZID, M., DAS, A. K., SHETTY, S., GOPE, P. & RODRIGUES, J. J. P. C. 2021. Security in 5G-Enabled Internet of Things Communication: Issues, Challenges, and Future Research Roadmap. IEEE Access, 9, 4466-4489, doi: https://doi.org/10.1109/ACCESS.2020.3047895.
XIAO, S., WANG, Z., SI, X. & LIU, G. 2024. Mean-square exponential stabilization of memristive neural networks: Dealing with replay attacks and communication interruptions. Communications in Nonlinear Science and Numerical Simulation, 138, 108188, doi: https://doi.org/10.1016/j.cnsns.2024.108188.
XING, Y., SHU, H. & KANG, F. 2023. PeerRemove: An adaptive node removal strategy for P2P botnet based on deep reinforcement learning. Computers & Security, 128, 103129, doi: https://doi.org/10.1016/j.cose.2023.103129.
YADAV, N., PANDE, S., KHAMPARIA, A. & GUPTA, D. 2022. Intrusion Detection System on IoT with 5G Network Using Deep Learning. Wireless Communications and Mobile Computing, 2022, 9304689, doi: https://doi.org/10.1155/2022/9304689.
ZAHRA, F. T., BOSTANCI, Y. S. & SOYTURK, M. 2023. Real-Time Jamming Detection in Wireless IoT Networks. IEEE Access, 11, 70425-70442, doi: https://doi.org/10.1109/ACCESS.2023.3293404.
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Accepted 2025-12-04
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