Anomaly Detection for 5G Networks: Enhancing Scalability, Responsiveness, and Operational Efficiency
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https://doi.org/10.52783/jns.v14.3185Keywords:
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The necessity of effective anomaly detection is brought to light by the fact that 5G networks are built to handle important application scenarios, as well as the complexity of the architecture and the vast amount of data flow inside them. When it comes to identifying irregularities, commonly known as network breakdowns or increases in congestion, standard methods typically fail to meet expectations. Quality of Service (QoS) and Quality of Experience (QoE) are both susceptible to being negatively impacted by the occurrence of these events. In order to enhance detection skills, there has been a trend toward more advanced approaches, such as deep learning (DL) combined with a variety of classification algorithms. This shift has occurred as a result of the challenge that has been presented. It has been demonstrated that systems that are based on deep learning can significantly improve accuracy. According to studies, some of these systems have achieved an accuracy rate of up to 98.8% and a false positive rate of only 0.44%. The use of deep neural networks (DNNs) is proven to be an effective solution for overcoming the limitations that are associated with traditional methods. The real call detail data (also known as CDRs) is utilized by these networks. A large increase in the intensity of anomaly detection is made possible by the integration of Mobile Edge Computing (MEC), which, in turn, provides real-time monitoring and rapid response. This is accomplished through the decentralization of processing activities. It is absolutely necessary to use this agile strategy in order to successfully handle the dynamic and ever-changing nature of 5G networks. Despite the emergence of new cyber threats and intricate network behaviors, this strategy will guarantee that service will continue to be provided without interruption. For the purpose of addressing the one-of-a-kind challenges that are presented by the next-generation of telecommunications infrastructures, future efforts should continue to improve these systems, with a particular emphasis on scalability, real-time responsiveness, and adaptive intelligence solutions. This will allow for the purpose of addressing them
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F. Chernogorov, S. Chernov, K. Brigatti, and T. Ristaniemi, ‘‘Sequencebased detection of sleeping cell failures in mobile networks,’’ Jan. 2015, arXiv:1501.03935. [Online]. Available: https://arxiv.org/abs/1501.03935
C. M. Mueller, M. Kaschub, C. Blankenhorns, and S. Wanke, ‘‘A cell outage detection algorithm using neighbor cell list reports,’’ in Proc. Self-Organizing Syst. 3rd Int. Workshop (IWSOS), Vienna, Austria, 2008, pp. 218–229
. A. Zoha, A. Saeed, A. Imran, M. A. Imran, and A. Abu-Dayya, ‘‘Datadriven analytics for automated cell outage detection in self-organizing networks,’’ in Proc. 11th Int. Conf. Design Reliable Commun. Netw. (DRCN), Kansas City, MO, USA, 2015, pp. 203–210.
A. Zoha, A. Saeed, A. Imran, M. A. Imran, and A. Abu-Dayya, ‘‘A SON solution for sleeping cell detection using low-dimensional embedding of MDT measurements,’’ in Proc. IEEE 25th Annu. Int. Symp. Pers., Indoor, Mobile Radio Commun. (PIMRC), Washington, DC, USA, Sep. 2014, pp. 1626–1630.
J. Turkka, F. Chernogorov, K. Brigatti, T. Ristaniemi, and J. Lempiäinen, ‘‘An approach for network outage detection from drive-testing databases,’’ J. Comput. Netw. Commun., vol. 2012, pp. 1–13, Sep. 2012, Art. no. 163184.
] W. A. Hapsari, A. Umesh, M. Iwamura, M. Tomala, B. Gyula, and B. Sebire, ‘‘Minimization of drive tests solution in 3GPP,’’ IEEE Commun. Mag., vol. 50, no. 6, pp. 28–36, Jun. 2012
B. Hussain, Q. Du, and P. Ren, ‘‘Deep learning-based big data-assisted anomaly detection in cellular networks,’’ in Proc. IEEE Global Commun. Conf. (GLOBECOM), Abu Dhabi, UAE, Dec. 2018, pp. 1–6
E. Ahmed, A. Ahmed, I. Yaqoob, J. Shuja, A. Gani, M. Imran, and M. Shoaib, ‘‘Bringing computation closer toward the user network: Is edge computing the solution?’’ IEEE Commun. Mag., vol. 55, no. 11, pp. 138–144, Nov. 2017.
Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, ‘‘A survey on mobile edge computing: The communication perspective,’’ IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322–2358, 4th Quart., 2017
Y. LeCun, Y. Bengio, and G. Hinton, ‘‘Deep learning,’’ Nature, vol. 521, no. 7553, pp. 436–444, May 2015
J. Patterson and A. Gibson, Deep Learning. Sebastopol, CA, USA: O’Reilly Media, 2017.
S. Jiang, J. Ferreira, and M. C. Gonzalez, ‘‘Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore,’’ IEEE Trans. Big Data, vol. 3, no. 2, pp. 208–219, Jun. 2017
A. Zoha, A. Saeed, H. Farooq, A. Rizwan, A. Imran, and M. A. Imran, ‘‘Leveraging intelligence from network CDR data for interference aware energy consumption minimization,’’ IEEE Trans. Mobile Comput., vol. 17, no. 7, pp. 1569–1582, Jul. 2018.
J. Hoy, Forensic Radio Survey Techniques for Cell Site Analysis. Chichester, U.K.: Wiley, 2015] W. Sun, X. Qin, S. Tang, and G. Wei, ‘‘A QoE anomaly detection and diagnosis framework for cellular network operators,’’ in Proc. IEEE Conf. Comput. Commun. Workshops (INFOCOM WKSHPS), Hong Kong, China, Apr./May 2015, pp. 450–455.
S. Papadopoulos, A. Drosou, and D. Tzovaras, ‘‘A novel graph-based descriptor for the detection of billing-related anomalies in cellular mobile networks,’’ IEEE Trans. Mobile Comput., vol. 15, no. 11, pp. 2655–2668, Nov. 2016.
] Y. Wang, Z. Wu, Q. Li, and Y. Zhu, ‘‘A model of telecommunication network performance anomaly detection based on service features clustering,’’ IEEE Access, vol. 5, pp. 17589–17596, Mar. 2017.
V. Chandola, A. Banerjee, and V. Kumar, ‘‘Anomaly detection: A survey,’’ ACM Comput. Surv., vol. 41, no. 3, pp. 15:1–15:58, 2009
Case Study: P3 Insights Help Swiss Operator Rise to the Top of the Mobile Network Pack, P3 Commun., Case Study, Aachen, Germany, Aug. 2017.
Ramneek, P. Hosein, W. Choi, and W. Seok, ‘‘Congestion detection for QoS-enabled wireless networks and its potential applications,’’ J. Commun. Netw., vol. 18, no. 3, pp. 513–522, Jun. 2016
Y. Li, B. Shen, J. Zhang, X. Gan, J. Wang, and X. Wang, ‘‘Offloading in HCNs: Congestion-aware network selection and user incentive design,’’ IEEE Trans. Wireless Commun., vol. 16, no. 10, pp. 6479–6492, Oct. 2017.
M. S. Parwez, D. Rawat, and M. Garuba, ‘‘Big data analytics for useractivity analysis and user-anomaly detection in mobile wireless network,’’ IEEE Trans. Ind. Informat., vol. 13, no. 4, pp. 2058–2065, Aug. 2017
] Sandvine, ‘‘Network congestion management: Considerations and techniques, version 2.0,’’ Sandvine, Waterloo, ON, Canada, White Paper, 2015. [Online]. Available: https://www.sandvine.com/hubfs/downloads/ archive/whitepaper-network-congestion-management.pdf
F. Chernogorov, S. Chernov, K. Brigatti, and T. Ristaniemi, ‘‘Sequencebased detection of sleeping cell failures in mobile networks,’’ Wireless Netw., vol. 22, no. 6, pp. 2029–2048, Aug. 2016.
F. Chernogorov, J. Turkka, T. Ristaniemi, and A. Averbuch, ‘‘Detection of sleeping cells in LTE networks using diffusion maps,’’ in Proc. IEEE 73rd Veh. Technol. Conf. (VTC Spring), Yokohama, Japan, May 2011, pp. 1–5.
] A. Imran, A. Zoha, and A. Abu-Dayya, ‘‘Challenges in 5G: How to empower SON with big data for enabling 5G,’’ IEEE Netw., vol. 28, no. 6, pp. 27–33, Nov./Dec. 2014.
B. Hussain, Q. Du, and P. Ren, ‘‘Semi-supervised learning based big datadriven anomaly detection in mobile wireless networks,’’ China Commun., vol. 15, no. 4, pp. 41–57, Apr. 2018.
U. Masood, A. Asghary, A. Imrany, and A. N. Mian, ‘‘Deep learning based detection of sleeping cells in next generation cellular networks,’’ in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2018, pp. 206–212.
A. Asghar, H. Farooq, and A. Imran, ‘‘Self-healing in emerging cellular networks: Review, challenges, and research directions,’’ IEEE Commun. Surveys Tuts., vol. 20, no. 3, pp. 1682–1709, 3rd Quart., 2018
Telecommunications—SMS, Call, Internet—MI. Accessed: Sep. 1, 2019. [Online]. Available: https://dandelion.eu/datagems/SpazioDati/telecomsms-call-internet-mi/descri
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Cambridge, MA, USA: MIT Press, 2016
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