Implications of Network Security in 5G Network and Detection of Cyber Attacks

Muhammad Imran Ghafoor, Mustafa Shakir, Shafi Ullah, Syed Aurangzeb, Mehmood Baryalai, Shariqa Fakhar, Abdul Wahid

Abstract


In recent years, cellular technology has grown in popularity, increasing the importance of 5G networks. Security standards are losing trust as large attacks and concerns about privacy increase. A number of fundamental security issues are addressed in this paper. Major telecommunications firms and academic studies provide this information. Many intrusion detection algorithms, tactics, and methods are applied to detect these attacks. As a result, machine learning is in its infancy. On the basis of data classification, machine learning can also make predictions about the future. Machine learning models are utilized to illustrate an intelligent intrusion detection system. A hybrid or advanced detection system cannot use the preceding approaches' detection algorithms. Using an open-source website, machine learning algorithms are trained and then automatically applied to new data. This investigation also examined machine learning techniques. A comparison of classifier accuracy is also conducted. Using K-Means clustering to core features of the UNSW-NB15NB15 and NSLKDD datasets, the ENN model performs well. Despite lower accuracy with a single hot encoding. With a precision of 99.7% on UNSWB and 99.72% on NSLKDD, the model correctly detects assaults in the 5G Network-based dataset.

Full Text:

PDF

References


D. Gupta, M. Gupta, S. Bhatt, and A. S. Tosun, “Detecting Anomalous User Behavior in Remote Patient Monitoring,” Proc. - 2021 IEEE 22nd Int. Conf. Inf. Reuse Integr. Data Sci. IRI 2021, pp. 33–40, 2021, doi: 10.1109/IRI51335.2021.00011.

Y. An, F. R. Yu, J. Li, J. Chen, and V. C. M. Leung, “for the Internet of Things ( IoT ),” vol. 8, no. 5, pp. 3554–3566, 2021.

S. Manimurugan, S. Al-Mutairi, M. M. Aborokbah, N. Chilamkurti, S. Ganesan, and R. Patan, “Effective attack detection in internet of medical things smart environment using a deep belief neural network,” IEEE Access, vol. 8, pp. 77396–77404, 2020, doi: 10.1109/ACCESS.2020.2986013.

N. Yadav, S. Pande, A. Khamparia, and D. Gupta, “Intrusion Detection System on IoT with 5G Network Using Deep Learning,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/9304689.

M. Wazid, A. K. Das, S. Shetty, P. Gope, and J. J. P. C. Rodrigues, “Security in 5G-Enabled Internet of Things Communication: Issues, Challenges and Future Research Roadmap,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3047895.

M. Wazid, A. K. Das, S. Shetty, P. Gope, and J. J. P. C. Rodrigues, “Security in 5G-Enabled Internet of Things Communication: Issues, Challenges and Future Research Roadmap,” IEEE Access, vol. 8, pp. 1–25, 2020, doi: 10.1109/ACCESS.2020.3047895.

Y.-E. Kim, Y.-S. Kim, and H. Kim, “Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network,” Sensors, vol. 22, no. 10, p. 3819, 2022, doi: 10.3390/s22103819.

S. Y. Alshunaifi, S. Mishra, and M. Alshehri, “Cyber-attack detection and mitigation using svm for 5g network,” Intell. Autom. Soft Comput., vol. 31, no. 1, pp. 13–28, 2022, doi: 10.32604/IASC.2022.019121.

M. Basnet and M. H. Ali, “Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning,” IET Gener. Transm. Distrib., vol. 15, no. 24, pp. 3435–3449, 2021, doi: 10.1049/gtd2.12275.

B. Alenazi and H. E. Idris, “Wireless Intrusion and Attack Detection for 5G Networks using Deep Learning Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 7, pp. 851–856, 2021, doi: 10.14569/ijacsa.2021.0120795.

J. Li, Z. Zhao, and R. Li, “Machine learning-based IDS for softwaredefined 5G network,” IET Networks, vol. 7, no. 2, pp. 53–60, 2018, doi: 10.1049/iet-net.2017.0212.

L. C. C. By-nc-sa, S. Date, P. Date, and C. Fouda, “A Novel Intrusion Detection System for Internet of Healthcare Things Based on Deep Subclasses Dispersion Information A Novel Intrusion Detection System for Internet of Healthcare Things Based on Deep Subclasses Dispersion Information,” pp. 0–16, 2022, doi: 10.36227/techrxiv.19292444.v1.

M. M. Kamruzzaman, “New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Smart Cities,” 2021 IEEE Globecom Work. GC Wkshps 2021 - Proc., vol. 2022, 2021, doi: 10.1109/GCWkshps52748.2021.9682055.

J. R. Jiang and Y. T. Chen, “Industrial Control System Anomaly Detection and Classification Based on Network Traffic,” IEEE Access, vol. 10, pp. 41874–41888, 2022, doi: 10.1109/ACCESS.2022.3167814.

J. Sen, “Security and privacy issues in cloud computing,” Archit. Protoc. Secur. Inf. Technol. Infrastructures, no. iv, pp. 1–45, 2013, doi: 10.4018/978-1-4666-4514-1.ch001.

I. Kaushik and N. Sharma, Black Hole Attack and Its Security Measure in Wireless Sensors Networks, vol. 1132. 2020.

A. M. Said, A. Yahyaoui, and T. Abdellatif, “Efficient anomaly detection for smart hospital iot systems,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–24, 2021, doi: 10.3390/s21041026.

L. Fang, Y. Li, Z. Liu, C. Yin, M. Li, and Z. J. Cao, “A Practical Model Based on Anomaly Detection for Protecting Medical IoT Control Services against External Attacks,” IEEE Trans. Ind. Informatics, vol. 17, no. 6, pp. 4260–4269, 2021, doi: 10.1109/TII.2020.3011444.

M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” Internet of Things (Netherlands), vol. 7, p. 100059, 2019, doi: 10.1016/j.iot.2019.100059.

Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, “Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection,” no. February, pp. 18–21, 2018, doi: 10.14722/ndss.2018.23204.

M. H. Ali et al., “Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT),” Electronics, vol. 11, no. 3, p. 494, 2022, doi: 10.3390/electronics11030494.

P. Fraga-Lamas, T. M. Fernández-Caramés, and L. Castedo, “Towards the internet of smart trains: A review on industrial IoT-connected railways,” Sensors (Switzerland), vol. 17, no. 6, 2017, doi: 10.3390/s17061457.

B. Ai, A. F. Molisch, M. Rupp, and Z. D. Zhong, “5G key technologies for smart railways,” Proc. IEEE, vol. 108, no. 6, pp. 856–893, 2020, doi: 10.1109/JPROC.2020.2988595.

I. Cvitic, D. Perakovic, B. B. Gupta, and K. K. R. Choo, “Boosting-Based DDoS Detection in Internet of Things Systems,” IEEE Internet Things J., vol. 9, no. 3, pp. 2109–2123, 2022, doi: 10.1109/JIOT.2021.3090909.

Ghafoor, M. I., Roomi, M. S., Aqeel, M., Sadiq, U., & Bazai, S. U. (2021, December). Multi-Features Classification of SMD Screen in Smart Cities using Randomised Machine Learning Algorithms. In 2021 2nd International Informatics and Software Engineering Conference (IISEC) (pp. 1-5). IEEE.

Jamil, A., ali Hameed, A., & Bazai, S. U. (2021). Land Cover Classification using Machine Learning Approaches from High Resolution Images. Journal of Applied and Emerging Sciences, 11(1), pp-108.

Feng, S., Liu, Q., Patel, A., Bazai, S. U., Jin, C. K., Kim, J. S., ... & Wilson, B. (2022). Automated pneumothorax triaging in chest X‐rays in the New Zealand population using deep‐learning algorithms. Journal of Medical Imaging and Radiation Oncology.

Hameed, M., Yang, F., Bazai, S. U., Ghafoor, M. I., Alshehri, A., Khan, I., ... & Andualem, M. (2022). Convolutional Autoencoder-Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset. Journal of Sensors, 2022.




DOI: http://dx.doi.org/10.36785/jaes.122561

Creative Commons License
Journal of Applied and Emerging Sciences by BUITEMS is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at www.buitms.edu.pk.
Permissions beyond the scope of this license may be available at http://journal.buitms.edu.pk/j/index.php/bj

Contacts | Feedback
© 2002-2014 BUITEMS