, Telecommunication management; Charging management; Charging Data Record (CDR) parameter description, 3GPP; Technical Specification Group Services and System Aspects, 2018.

, 5G vision, white paper, Samsung Electronics Co., Tech. Rep, 2015.

, 5G; Service requirements for next generation new services and markets, 2018.

, 5G: A technology vision, white paper, Huawei Technologies Co., Tech. Rep, 2013.

P. S. Addison, The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance, 2017.

B. Agarwal and N. , Hybrid approach for detection of anomaly network traffic using data mining techniques, Procedia Technology, 2012.

A. Agresti, An introduction to categorical data analysis, 2018.

M. Ahmed, A. N. Mahmood, and J. Hu, A survey of network anomaly detection techniques, Journal of Network and Computer Applications, 2016.

G. A. Akpakwu, B. J. Silva, G. P. Hancke, and A. M. Abu-mahfouz, A survey on 5G networks for the internet of things: Communication technologies and challenges, IEEE Access, 2018.

S. Axelsson, Intrusion detection systems: A survey and taxonomy, 2000.

P. Barford, J. Kline, D. Plonka, and A. Ron, A signal analysis of network traffic anomalies, Proc. of ACM SIGCOMM Workshop IMW, 2002.

P. Berezi?ski, B. Jasiul, and M. Szpyrka, An entropy-based network anomaly detection method, Entropy, 2015.

G. Bergland, A guided tour of the fast fourier transform, IEEE spectrum, 1969.

M. Bianchini and F. Scarselli, On the complexity of neural network classifiers: A comparison between shallow and deep architectures, IEEE Transactions on Neural Networks and Learning Systems, 2014.

H. Bozdogan, Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions, Psychometrika, 1987.

A. P. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern recognition, 1997.

S. Brady, D. Magoni, J. Murphy, H. Assem, and A. O. Portillo-dominguez, Analysis of machine learning techniques for anomaly detection in the internet of things, Latin American Conference on Computational Intelligence (LA-CCI), 2018.

D. Brauckhoff, X. Dimitropoulos, A. Wagner, and K. Salamatian, Anomaly extraction in backbone networks using association rules, Proceedings of the ACM SIGCOMM conference on Internet measurement, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00527139

P. J. Brockwell, R. A. Davis, and M. V. Calder, Introduction to time series and forecasting, 2002.

M. Cairo, G. Farina, and R. Rizzi, Decoding hidden markov models faster than viterbi via online matrix-vector (max, +)-multiplication, AAAI Conference on Artificial Intelligence, 2016.

R. N. Calheiros, E. Masoumi, R. Ranjan, and R. Buyya, Workload prediction using ARIMA model and its impact on cloud applications' QoS, IEEE Transactions on Cloud Computing, 2015.

G. Casella and R. L. Berger, Statistical inference, 2002.

M. Çelik, F. Dada?er-Çelik, and A. ?. Dokuz, Anomaly detection in temperature data using dbscan algorithm, International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 2011.

V. Chandola, A. Banerjee, and V. Kumar, Outlier detection: A survey, ACM Computing Surveys, 2007.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM computing surveys (CSUR), 2009.

C. Chang and C. Lin, LIBSVM: a library for support vector machines, 2011.

W. G. Cochran, Sampling techniques, 2007.

A. Aspremont, F. R. Bach, and L. E. Ghaoui, Full regularization path for sparse principal component analysis, International Conference on Machine Learning, 2007.

J. Davis and M. Goadrich, The relationship between Precision-Recall and ROC curves, International conference on Machine learning, 2006.

W. H. Day and H. Edelsbrunner, Efficient algorithms for agglomerative hierarchical clustering methods, Journal of Classification, 1984.

, Digital cellular telecommunications system

G. Dimopoulos, I. Leontiadis, P. Barlet-ros, K. Papagiannaki, and P. Steenkiste, Identifying the root cause of video streaming issues on mobile devices, 2015.

J. J. Dongarra and F. Sullivan, Guest editors introduction to the top 10 algorithms, Computing in Science and Engineering, 2000.

Z. Du, L. Ma, H. Li, Q. Li, G. Sun et al., Network traffic anomaly detection based on wavelet analysis, International Conference on Software Engineering Research, Management and Applications, 2018.

O. Eluwole, N. Udoh, M. Ojo, C. Okoro, and A. Akinyoade, From 1G to 5G, what next?, IAENG International Journal of Computer Science, 2018.

J. M. Estevez-tapiador, P. Garcia-teodoro, and J. E. Diaz-verdejo, Anomaly detection methods in wired networks: A survey and taxonomy, Computer Communications, 2004.

, Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description, 2010.

S. Feng and E. Seidel, Self-organizing networks (son) in 3gpp long term evolution, Nomor Research GmbH, 2008.

R. Fontugne and K. Fukuda, A Hough-transform-based anomaly detector with an adaptive time interval, Proc. of ACM SAC, 2011.

S. Fortes, A. Garcia, J. A. Fernandez-luque, A. Garrido, and R. Barco, Context-aware self-healing: User equipment as the main source of information for small-cell indoor networks, IEEE Vehicular Technology Magazine, 2016.

R. Froehlich, Knowledge base radio and core network prescriptive root cause analysis, US Patent Application 14/621, vol.101, 2016.

M. M. Fuad and P. Marteau, Towards a faster symbolic aggregate approximation method, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00690016

S. R. Gaddam, V. V. Phoha, and K. S. Balagani, K-means+id3: A novel method for supervised anomaly detection by cascading k-means clustering and ID3 decision tree learning methods, IEEE Trans. Knowl. Data Eng, 2007.

P. Garcia-teodoro, J. Diaz-verdejo, G. Maciá-fernández, and E. Vázquez, Anomalybased network intrusion detection: Techniques, systems and challenges, 2009.

A. Garg, Digital society from 1G to 5G: A comparative study, International Journal of Application or Innovation in Engineering & Management, 2014.

G. Giacinto, F. Roli, and L. Didaci, Fusion of multiple classifiers for intrusion detection in computer networks, Pattern recognition letters, 2003.

P. Gogoi, R. Das, B. Borah, and D. K. Bhattacharyya, Efficient rule set generation using rough set theory for classification of high dimensional data, 2011.

A. Gómez-andrades, P. M. Luengo, I. Serrano, and R. Barco, Automatic root cause analysis for LTE networks based on unsupervised techniques, IEEE Trans. Vehicular Technology, 2016.

, GSM/EDGE radio access network (GERAN); overall description, 2009.

F. Guigou, P. Collet, and P. Parrend, Anomaly detection and motif discovery in symbolic representations of time series, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01507517

F. Guigou, P. Collet, and P. Parrend, The artificial immune ecosystem: A bioinspired meta-algorithm for boosting time series anomaly detection with expert input, Applications of Evolutionary Computation, 2017.

H. Guo and C. S. Burrus, Wavelet transform based fast approximate Fourier transform, IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997.

M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, Outlier detection for temporal data: A survey, IEEE Transactions on Knowledge and Data Engineering, 2014.

B. Hamzeh, Network failure detection and prediction using signal measurements, Patent US9100339B1, 2014.

T. Han, Y. Lan, L. Xiao, B. Huang, and K. Zhang, Event detection with vector similarity based on fourier transformation, Proc. of IEEE ICCSSE, 2014.

A. Hanemann, A hybrid rule-based/case-based reasoning approach for service fault diagnosis, International Conference on Advanced Information Networking and Applications AINA, 2006.

F. Van-harmelen, V. Lifschitz, and B. W. Porter, Handbook of Knowledge Representation, 2008.

F. Harrou, F. Kadri, S. Chaabane, C. Tahon, and Y. Sun, Improved principal component analysis for anomaly detection: Application to an emergency department, Computers & Industrial Engineering, 2015.

N. A. Heard, D. J. Weston, K. Platanioti, and D. J. Hand, Bayesian anomaly detection methods for social networks, The Annals of Applied Statistics, 2010.

J. J. Hildebrand, System and method for allocating resources based on events in a network environment, Patent US8788654B2, 2010.

B. Horst and K. Abraham, Data mining in time series databases, 2004.

W. Hu, Y. Liao, and V. R. Vemuri, Robust support vector machines for anomaly detection in computer security, Proc. of IEEE ICMLA, 2003.

Y. Huang, Service problem diagnosis for mobile wireless networks, 2016.

M. A. Imran and A. Zoha, Challenges in 5g: How to empower SON with big data for enabling 5g, IEEE Network, 2014.

Z. Jadidi, V. Muthukkumarasamy, E. Sithirasenan, and M. Sheikhan, Flow-based anomaly detection using neural network optimized with GSA algorithm, Proc. of IEEE ICDCS, 2013.

A. Y. Javaid, Q. Niyaz, W. Sun, and M. Alam, A deep learning approach for network intrusion detection system, ICST Trans. Security Safety, 2016.

Y. Jin, N. G. Duffield, A. Gerber, P. Haffner, S. Sen et al., Nevermind, the problem is already fixed: Proactively detecting and troubleshooting customer DSL problems, 2010.

C. Kane, System and method for identifying problems on a network, 2015.

W. Kellerer, A. Basta, P. Babarczi, A. Blenk, M. He et al., How to measure network flexibility? a proposal for evaluating softwarized networks, IEEE Communications Magazine, 2018.

E. J. Keogh, J. Lin, and A. W. Fu, HOT SAX: efficiently finding the most unusual time series subsequence, Proc. of IEEE ICDM, 2005.

E. Keogh, S. Chu, D. Hart, and M. Pazzani, Segmenting time series: A survey and novel approach, Data mining in time series databases, 2004.

S. Khalid, T. Khalil, and S. Nasreen, A survey of feature selection and feature extraction techniques in machine learning, Science and Information Conference, 2014.

L. Khan, M. Awad, and B. Thuraisingham, A new intrusion detection system using support vector machines and hierarchical clustering, The VLDB journal, 2007.

E. J. Khatib, R. Barco, A. Gómez-andrades, P. M. Luengo, and I. Serrano, Data mining for fuzzy diagnosis systems in LTE networks, Expert Syst. Appl, 2015.

E. J. Khatib, R. Barco, P. M. Luengo, I. De-la-bandera, and I. Serrano, Self-healing in mobile networks with Big Data, IEEE Communications Magazine, 2016.

E. J. Khatib, Data analytics and knowledge discovery for root cause analysis in lte self-organizing networks, 2017.

D. B. Kiesekamp, T. Pilon, and R. Bolder, System and method of visualizing most unhealthy network elements within a network or data center, 2016.

G. Kim, S. Lee, and S. Kim, A novel hybrid intrusion detection method integrating anomaly detection with misuse detection, Expert Systems with Applications, 2014.

P. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza, A survey of machine learning techniques applied to self-organizing cellular networks, IEEE Communications Surveys and Tutorials, 2017.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, International Joint Conference on Artificial Intelligence, 1995.

A. Lakhina, M. Crovella, and C. Diot, Diagnosing network-wide traffic anomalies, Proc. of ACM SIGCOMM, 2004.

O. Leifler, Comparison of lem2 and a dynamic reduct classification algorithm, 2002.

F. Li and M. Thottan, End-to-end service quality measurement using sourcerouted probes, IEEE International Conference on Computer Communications (IN-FOCOM), 2006.

J. Li, W. Pedrycz, and I. Jamal, Multivariate time series anomaly detection: A framework of hidden markov models, Appl. Soft Comput, 2017.

J. Liu, F. Liu, and N. Ansari, Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop, IEEE Network, 2014.

X. Liu, G. Chuai, W. Gao, and K. Zhang, GA-AdaBoostSVM classifier empowered wireless network diagnosis, EURASIP Journal on Wireless Comm. and Networking, 2018.

, Looking ahead to 5G, white paper, 2014.

, ); Selfconfiguring and self-optimizing network (SON) use cases and solutions, 2010.

W. Lu and A. A. Ghorbani, Network anomaly detection based on wavelet analysis, EURASIP J. Adv. Sig. Proc, 2009.

P. M. Luengo, R. Barco, E. Cruz, A. Gómez-andrades, E. J. Khatib et al., A method for identifying faulty cells using a classification tree-based UE diagnosis in LTE, EURASIP J. Wireless Comm. and Networking, 2017.

J. Luo and S. M. Bridges, Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection, International Journal of Intelligent Systems, 2000.

J. Ma and S. Perkins, Time-series novelty detection using one-class support vector machines, Proc. of IEEE-INNS IJCNN, 2003.

A. Mahapatro and P. M. Khilar, Fault diagnosis in wireless sensor networks: A survey, IEEE Communications Surveys and Tutorials, 2013.

A. A. Mahimkar, Z. Ge, A. Shaikh, J. Wang, J. Yates et al., Towards automated performance diagnosis in a large IPTV network, SIGCOMM, 2009.

A. Mahimkar, Z. Ge, J. Wang, J. Yates, Y. Zhang et al., Rapid detection of maintenance induced changes in service performance, 2011.

P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, Long short term memory networks for anomaly detection in time series, European Symposium on Artificial Neural Networks, 2015.

S. Mascaro, A. E. Nicholson, and K. B. Korb, Anomaly detection in vessel tracks using Bayesian networks, Int. J. Approx. Reasoning, 2014.

M. Mdini, A. Blanc, G. Simon, J. Barotin, and J. Lecoeuvre, Monitoring the network monitoring system: Anomaly detection using pattern recognition, IM, 2017.

M. Mdini, G. Simon, A. Blanc, and J. Lecoeuvre, ARCD: A solution for root cause diagnosis in mobile networks, CNSM, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01962869

H. Mi, H. Wang, Y. Zhou, M. R. Lyu, and H. Cai, Toward fine-grained, unsupervised, scalable performance diagnosis for production cloud computing systems, IEEE Trans. Parallel Distrib. Syst, 2013.

H. Z. Moayedi and M. A. Masnadi-shirazi, ARIMA model for network traffic prediction and anomaly detection, Proc. of IEEE ISCIT, 2008.

M. A. Monge, J. M. Vidal, and L. J. Garc?a-villalba, Reasoning and knowledge acquisition framework for 5g network analytics, 2017.

J. Moysen and L. Giupponi, A reinforcement learning based solution for selfhealing in LTE networks, Vehicular Technology Conference, 2014.

S. Mukkamala, G. Janoski, and A. Sung, Intrusion detection using neural networks and support vector machines, Proc. of IEEE-INNS IJCNN, 2002.

A. P. Muniyandi, R. Rajeswari, and R. Rajaram, Network Anomaly Detection by Cascading k-Means Clustering and c4.5 Decision Tree algorithm, Procedia Engineering, 2012.

G. Münz, S. Li, and G. Carle, Traffic Anomaly Detection Using KMeans Clustering, Proc. of In GI/ITG Workshop MMBnet, 2007.

K. Nagaraj, C. E. Killian, and J. Neville, Structured comparative analysis of systems logs to diagnose performance problems, NSDI, 2012.

D. Noll, Identification of storage system elements causing performance degradation, 2016.

G. Nychis, V. Sekar, D. G. Andersen, H. Kim, and H. Zhang, An empirical evaluation of entropy-based traffic anomaly detection, Proceedings of the ACM SIGCOMM conference on Internet measurement, 2008.

P. Ong, Y. Choo, and A. Muda, A manufacturing failure root cause analysis in imbalance data set using pca weighted association rule mining, Jurnal Teknologi, 2015.

M. K. Pakhira, A linear time-complexity k-means algorithm using cluster shifting, International Conference on Computational Intelligence and Communication Networks, 2014.

D. Palacios, I. De-la-bandera, A. Gómez-andrades, L. Flores, and R. Barco, Automatic feature selection technique for next generation self-organizing networks, IEEE Communications Letters, 2018.

D. Palacios, E. J. Khatib, and R. Barco, Combination of multiple diagnosis systems in self-healing networks, Expert Syst. Appl, 2016.

A. Patcha and J. Park, An overview of anomaly detection techniques: Existing solutions and latest technological trends, 2007.

E. H. Pena, M. V. De-assis, M. Lemes, and P. , Anomaly detection using forecasting methods ARIMA and HWDS, Proc. of IEEE SCCC, 2013.

R. Perdisci, D. Ariu, P. Fogla, G. Giacinto, and W. Lee, Mcpad: A multiple classifier system for accurate payload-based anomaly detection, 2009.

K. Potdar, T. S. Pardawala, and C. D. Pai, A comparative study of categorical variable encoding techniques for neural network classifiers, International Journal of Computer Applications, 2017.

H. Ren, Z. Ye, and Z. Li, Anomaly detection based on a dynamic markov model, Inf. Sci, 2017.

H. Ringberg, A. Soule, J. Rexford, and C. Diot, Sensitivity of PCA for traffic anomaly detection, Proc. of ACM SIGMETRICS, 2007.

S. F. Rodriguez, R. Barco, and A. Aguilar-garc?a, Location-based distributed sleeping cell detection and root cause analysis for 5g ultra-dense networks, EURASIP J. Wireless Comm. and Networking, 2016.

S. F. Rodriguez, R. Barco, A. Aguilar-garc?a, and P. M. Luengo, Contextualized indicators for online failure diagnosis in cellular networks, Computer Networks, 2015.

R. Sekar, A. Gupta, J. Frullo, T. Shanbhag, A. Tiwari et al., Specification-based anomaly detection: A new approach for detecting network intrusions, ACM conference on Computer and communications security, 2002.

I. Garcia, R. Moreno, E. Khatiband, P. Munoz-luengo, and I. ,

B. De-la and . Cascales, Fault Diagnosis in Networks, Patent Application WO2016169616A1, 2016.

, Telecommunication management; Self-Organizing Networks (SON); Self-healing concepts and requirements, 2012.

A. Sharma and P. K. Panigrahi, A neural network based approach for predicting customer churn in cellular network services, 2013.

T. Shon and J. Moon, A hybrid machine learning approach to network anomaly detection, Information Sciences, 2007.

S. Singh and P. Singh, Key concepts and network architecture for 5G mobile technology, International Journal of Scientific Research Engineering & Technology (IJSRET), 2012.

M. Sokolova, N. Japkowicz, and S. Szpakowicz, Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation, Australasian joint conference on artificial intelligence, 2006.

X. Song, M. Wu, C. Jermaine, and S. Ranka, Conditional anomaly detection, IEEE Transactions on Knowledge and Data Engineering, 2007.

V. A. Sotiris, P. W. Tse, and M. G. Pecht, Anomaly detection through a bayesian support vector machine, IEEE Trans. Reliability, 2010.

G. Stein, B. Chen, A. S. Wu, and K. A. Hua, Decision tree classifier for network intrusion detection with ga-based feature selection, Annual Southeast Regional Conference, 2005.

M. Steinder and A. S. Sethi, A survey of fault localization techniques in computer networks, Sci. Comput. Program, 2004.

J. Su and H. Zhang, A fast decision tree learning algorithm, National Conference on Artificial Intelligence and Innovative Applications of Artificial Intelligence Conference, 2006.

T. A. Tang, L. Mhamdi, D. C. Mclernon, S. A. Zaidi, and M. Ghogho, Deep learning approach for network intrusion detection in software defined networking, International Conference on Wireless Networks and Mobile Communications, WINCOM, 2016.

T. M. Thang and J. Kim, The anomaly detection by using dbscan clustering with multiple parameters, International Conference on Information Science and Applications (ICISA), 2011.

C. Tselios and G. Tsolis, On QoE-awareness through virtualized probes in 5G networks, IEEE International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2016.

, Universal Mobile Telecommunications System (UMTS

, Telecommunication management; Subscriber and equipment trace; Trace control and configuration management, 2005.

, Universal mobile telecommunications system (UMTS)

, UTRAN overall description, 2009.

J. J. Verbeek, N. A. Vlassis, and B. J. Kröse, Efficient greedy learning of Gaussian mixture models, Neural Computation, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00321487

P. Viswanath and R. Pinkesh, l-DBSCAN : A fast hybrid density based clustering method, International Conference on Pattern Recognition ICPR, 2006.

A. Wagner and B. Plattner, Entropy based worm and anomaly detection in fast IP networks, International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprise, 2005.

I. H. Witten, E. Frank, M. A. Hall, and C. J. , Data Mining: Practical machine learning tools and techniques, 2016.

M. Wo?niak, M. Graña, and E. Corchado, A survey of multiple classifier systems as hybrid systems, 2014.

Y. Xu, Y. Sun, J. Wan, X. Liu, and Z. Song, Industrial Big Data for fault diagnosis: Taxonomy, review, and applications, 2017.

M. J. Zaki, Generating non-redundant association rules, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.

J. Zhang, Advancements of outlier detection: A survey, ICST Transactions on Scalable Information Systems, 2013.

J. Zhang and N. Ansari, On assuring end-to-end QoE in next generation networks: Challenges and a possible solution, IEEE Communications Magazine, 2011.

J. Zhang, M. Zulkernine, and A. Haque, Random-forests-based network intrusion detection systems, IEEE Trans. Systems, Man, and Cybernetics, 2008.

Z. Zheng, L. Yu, Z. Lan, and T. Jones, 3-dimensional root cause diagnosis via co-analysis, ICAC, 2012.

B. Zhu and S. Sastry, Revisit dynamic ARIMA based anomaly detection, Proc. of IEEE PASSAT/SocialCom, 2011.

H. Zou, T. Hastie, and R. Tibshirani, -Anomaly Detection and Root Cause Analysis, Journal of Computational and Graphical Statistics, 2006.