Е-Публикации
Технически университет - София

Abstract: Streaming data are present all around us. From traditional radio systems streaming audio to today’s connected end-user devices constantly sending information or accessing services, data are flowing constantly between nodes across various networks. The demand for appropriate outlier detection (OD) methods in the fields of fault detection, special events detection, and malicious activities detection and prevention is not only persistent over time but increasing, especially with the recent developments in Telecommunication systems such as Fifth Generation (5G) networks facilitating the expansion of the Internet of Things (IoT). The process of selecting a computationally efficient OD method, adapted for a specific field and accounting for the existence of empirical data, or lack thereof, is non-trivial. This paper presents a thorough survey of OD methods, categorized by the applications they are implemented in, the basic assumptions that they use according to the characteristics of the streaming data, and a summary of the emerging challenges, such as the evolving structure and nature of the data and their dimensionality and temporality. A categorization of commonly used datasets in the context of streaming data is produced to aid data source identification for researchers in this field. Based on this, guidelines for OD method selection are defined, which consider flexibility and sample size requirements and facilitate the design of such algorithms in Telecommunications and other industries.

References

1. Wang Y. Li J. Yang B. Li H.G. Stream-data-clustering based adaptive alarm threshold setting approaches for industrial processes with multiple operating conditions. ISA Transactions ISA Trans. 2022 129 594 608 10.1016/j.isatra.2022.01.030
2. Zhu R. Ji X. Yu D. Tan Z. Zhao L. Li J. Xia X. KNN-based approximate outlier detection algorithm over IoT streaming data IEEE Access 2020 8 42749 42759 10.1109/ACCESS.2020.2977114
3. Paul K. Chatterjee S.S. Pai P. Varshney A. Juikar S. Prasad V. Bhadra B. Dasgupta S. Viable smart sensors and their application in data driven agriculture Comput. Electron. Agric. 2022 198 107096 10.1016/j.compag.2022.107096
4. Yang Y. Ding S. Liu Y. Meng S. Chi X. Ma R. Yan C. Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse Digit. Commun. Netw. 2021 8 498 507 10.1016/j.dcan.2021.11.004
5. Juszczuk P. Kozak J. Kania K. Using similarity measures in prediction of changes in financial market stream data—Experimental approach Data Knowl. Eng. 2020 125 101782 10.1016/j.datak.2019.101782
6. Edge M.E. Sampaio P.R.F. The design of FFML: A rule-based policy modelling language for proactive fraud management in financial data streams Expert Syst. Appl. 2012 39 9966 9985 10.1016/j.eswa.2012.01.143
7. Ma B. Guo W. Zhang J. A survey of online data-driven proactive 5G network optimisation using machine learning IEEE Access 2020 8 35606 35637 10.1109/ACCESS.2020.2975004
8. Parwez M.S. Rawat D.B. Garuba M. Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network IEEE Trans. Ind. Inform. 2017 13 2058 2065 10.1109/TII.2017.2650206
9. Ullah I. Mahmoud Q.H. A framework for anomaly detection in IoT networks using conditional generative adversarial networks IEEE Access 2021 9 165907 165931 10.1109/ACCESS.2021.3132127
10. Márquez D.G. Otero A. Félix P. García C.A. A novel and simple strategy for evolving prototype based clustering Pattern Recognit. 2018 82 16 30 10.1016/j.patcog.2018.04.020
11. ZareMoodi P. Kamali Siahroudi S. Beigy H. Concept-evolution detection in non-stationary data streams: A fuzzy clustering approach Knowl. Inf. Syst. 2019 60 1329 1352 10.1007/s10115-018-1266-y
12. Chan H.L. Lam T.W. Lee L.K. Ting H.F. Continuous monitoring of distributed data streams over a time-based sliding window Algorithmica 2012 62 1088 1111 10.1007/s00453-011-9506-5
13. Pugliese L.D.P. Ferone D. Festa P. Guerriero F. Shortest path tour problem with time windows Eur. J. Oper. Res. 2020 282 334 344 10.1016/j.ejor.2019.08.052
14. Blevins D.H. Moriano P. Bridges R.A. Verma M.E. Iannacone M.D. Hollifield S.C. Time-based can intrusion detection benchmark arXiv 2021 2101.05781
15. Yue W. Moczalla R. Luthra M. Rabl T. Deco: Fast and Accurate Decentralized Aggregation of Count-Based Windows in Large-Scale IoT Applications Proceedings of the 27th International Conference on Extending Database Technology (EDBT) Paestum, Italy 25–28 March 2024 412 425
16. Zeng Z. Cui L. Qian M. Zhang Z. Wei K. A survey on sliding window sketch for network measurement Computer Networks 2023 226 109696 10.1016/j.comnet.2023.109696
17. Baldini G. Amerini I. Online Distributed Denial of Service (DDoS) intrusion detection based on adaptive sliding window and morphological fractal dimension Comput. Netw. 2022 210 108923 10.1016/j.comnet.2022.108923
18. Iqbal W. Berral J.L. Carrera D. Adaptive sliding windows for improved estimation of data center resource utilization Future Gener. Comput. Syst. 2020 104 212 224
19. Youn J. Shim J. Lee S.G. Efficient data stream clustering with sliding windows based on locality-sensitive hashing IEEE Access 2018 6 63757 63776 10.1109/ACCESS.2018.2877138
20. Bahri M. Bifet A. Gama J. Gomes H.M. Maniu S. Data stream analysis: Foundations, major tasks and tools Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2021 11 1405 10.1002/widm.1405
21. Baek Y. Yun U. Kim H. Nam H. Lee G. Yoon E. Vo B. Lin J.C.W. Erasable pattern mining based on tree structures with damped window over data streams Eng. Appl. Artif. Intell. 2020 94 103735 10.1016/j.engappai.2020.103735
22. Kim J. Yun U. Kim H. Ryu T. Lin J.C.W. Fournier-Vier P. Pedrycz W. Average utility driven data analytics on damped windows for intelligent systems with data streams Int. J. Intell. Syst. 2021 36 5741 5769 10.1002/int.22528
23. Zubaroğlu A. Atalay V. Data stream clustering: A review Artif. Intell. Rev. 2021 54 1201 1236 10.1007/s10462-020-09874-x
24. Tanbeer S.K. Ahmed C.F. Jeong B.S. Lee Y.K. Sliding window-based frequent pattern mining over data streams Inf. Sci. 2009 179 3843 3865 10.1016/j.ins.2009.07.012
25. Giraud C. Introduction to High-Dimensional Statistics Chapman and Hall/CRC Boca Raton, FL, USA 2021
26. Assent I. Clustering high dimensional data Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2012 2 340 350 10.1002/widm.1062
27. Peng W. Zhou T. Chen Y. Enhancing mass spectrometry data analysis: A novel framework for calibration, outlier detection, and classification Pattern Recognit. Lett. 2024 182 1 8 10.1016/j.patrec.2024.03.026
28. Harrou F. Bouyeddou B. Zerrouki N. Dairi A. Sun Y. Zerrouki Y. Detecting the signs of desertification with Landsat imagery: A semi-supervised anomaly detection approach Results Eng. 2024 22 102037 10.1016/j.rineng.2024.102037
29. Tahvili S. Hatvani L. Chapter three-transformation, vectorization, and optimization Artificial Intelligence Methods for Optimization of the Software Testing Process, Ser. Uncertainty, Computational Techniques, and Decision Intelligence Academic Press Cambridge, MA, USA 2022 35 84
30. Rozza A. Lombardi G. Ceruti C. Casiraghi E. Campadelli P. Novel high intrinsic dimensionality estimators Mach. Learn. 2012 89 37 65 10.1007/s10994-012-5294-7
31. Hawkins D.M. Identification of Outliers Chapman and Hall London, UK 1980 Volume 11
32. Aggarwal C.C. Data Mining: The Textbook Springer New York, NY, USA 2015 Volume 1
33. Smiti A. A critical overview of outlier detection methods Comput. Sci. Rev. 2020 38 100306 10.1016/j.cosrev.2020.100306
34. Škoda P. Adam F. Knowledge Discovery in Big Data from Astronomy and Earth Observation Elsevier Amsterdam, The Netherlands 2020
35. Han J. Kamber M. Pei J. Outlier Detection, The Morgan Kaufmann Series in Data Management Systems Data Mining 3rd ed. Morgan Kaufmann Burlington, MA, USA 2012 543 584
36. Gupta M. Gao J. Aggarwal C.C. Han J. Outlier detection for temporal data: A survey IEEE Trans. Knowl. Data Eng. 2013 26 2250 2267 10.1109/TKDE.2013.184
37. Shi Y. Gong J. Deng M. Yang X. Xu F. A graph-based approach for detecting spatial cross-outliers from two types of spatial point events Comput. Environ. Urban Syst. 2018 72 88 103 10.1016/j.compenvurbsys.2018.05.011
38. Zheng Y. Zhang H. Yu Y. Detecting collective anomalies from multiple spatio-temporal datasets across different domains Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Seattle, WA, USA 3–6 November 2015 ACM New York, NY, USA 2015 1 10
39. Qin S.J. Neural networks for intelligent sensors and control—Practical issues and some solutions Neural Systems for Control Academic Press Cambridge, MA, USA 1997 213 234
40. Han J. Pei J. Yin Y. Mao R. Mining frequent patterns without candidate generation: A frequent-pattern tree approach Data Min. Knowl. Discov. 2004 8 53 87 10.1023/B:DAMI.0000005258.31418.83
41. Keogh E. Lonardi S. Chiu B.Y.C. Finding surprising patterns in a time series database in linear time and space Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Edmonton, AL, Canada 23–26 July 2002 550 556
42. Kern R. Al-Ubaidi T. Sabol V. Krebs S. Khodachenko M. Scherf M. Astro-and Geoinformatics–Visually Guided Classification of Time Series Data Knowledge Discovery in Big Data From Astronomy and Earth Observation Elsevier Amsterdam, The Netherlands 2020 267 282
43. Knapp E.D. Langill J. Industrial Network Security: Securing Critical Infrastructure Networks for Smart Grid, SCADA, and Other Industrial Control Systems Syngress Oxford, UK 2014
44. Kotu V. Deshpande B. Data Science: Concepts and Practice Morgan Kaufmann Burlington, MA, USA 2018
45. Duraj A. Szczepaniak P.S. Outlier detection in data streams—A comparative study of selected methods Procedia Comput. Sci. 2021 192 2769 2778 10.1016/j.procs.2021.09.047
46. Fernandes G. Rodrigues J.J. Carvalho L.F. Al-Muhtadi J.F. Proença M.L. A comprehensive survey on network anomaly detection Telecommun. Syst. 2019 70 447 489 10.1007/s11235-018-0475-8
47. Dwivedi R.K. Rai A.K. Kumar R. Outlier detection in wireless sensor networks using machine learning techniques: A survey Proceedings of the 2020 International Conference on Electrical and Electronics Engineering (ICE3) Gorakhpur, India 14–15 February 2020 IEEE Piscataway, NY, USA 2020 316 321
48. Wang H. Bah M.J. Hammad M. Progress in outlier detection techniques: A survey IEEE Access 2019 7 107964 108000 10.1109/ACCESS.2019.2932769
49. Habeeb R.A.A. Nasaruddin F. Gani A. Hashem I.A.T. Ahmed E. Imran M. Real-time big data processing for anomaly detection: A survey Int. J. Inf. Manag. 2019 45 289 307 10.1016/j.ijinfomgt.2018.08.006
50. Samara M.A. Bennis I. Abouaissa A. Lorenz P. A survey of outlier detection techniques in IoT: Review and classification J. Sens. Actuator Netw. 2022 11 4 10.3390/jsan11010004
51. Gaddam A. Wilkin T. Angelova M. Gaddam J. Detecting sensor faults, anomalies and outliers in the internet of things: A survey on the challenges and solutions Electronics 2020 9 511 10.3390/electronics9030511
52. Souiden I. Omri M.N. Brahmi Z. A survey of outlier detection in high dimensional data streams Comput. Sci. Rev. 2022 44 100463 10.1016/j.cosrev.2022.100463
53. Molugaram K. Rao G.S. Shah A. Davergave N. Statistical Techniques for Transportation Engineering Butterworth-Heinemann Portsmouth, NH, USA 2017
54. Ryu M. Lee G. Lee K. Online sequential extreme studentized deviate tests for anomaly detection in streaming data with varying patterns Clust. Comput. 2021 24 1975 1987 10.1007/s10586-021-03236-0
55. Leys C. Ley C. Klein O. Bernard P. Licata L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median J. Exp. Soc. Psychol. 2013 49 764 766 10.1016/j.jesp.2013.03.013
56. Bhargavi M.V. Sireesha V. A comparative study for statistical outlier detection using colon cancer data Adv. Appl. Stat. 2022 72 41 54 10.17654/0972361722003
57. Vieira R.G. Leone Filho M.A. Semolini R. An Enhanced Seasonal-Hybrid ESD technique for robust anomaly detection on time series Symp. Bras. Redes Comput. Sist. Distrib. 2018 281 294 10.5753/sbrc.2018.2422
58. Ray S. McEvoy D.S. Aaron S. Hickman T.T. Wright A. Using statistical anomaly detection models to find clinical decision support malfunctions J. Am. Med. Inform. Assoc. 2018 25 862 871 10.1093/jamia/ocy041 29762678
59. Saleem S. Aslam M. Shaukat M.R. A Review and Empirical Comparison of univariate outlier Detection Methods Pak. J. Stat. 2021 37 447 462
60. Bhattacharya S. Beirlant J. Outlier detection and a tail-adjusted boxplot based on extreme value theory arXiv 2019 1912.02595
61. Dai W. Mrkvička T. Sun Y. Genton M.G. Functional outlier detection and taxonomy by sequential transformations Comput. Stat. Data Anal. 2020 149 106960 10.1016/j.csda.2020.106960
62. Walker M.L. Dovoedo Y.H. Chakraborti S. Hilton C.W. An improved boxplot for univariate data Am. Stat. 2018 72 348 353 10.1080/00031305.2018.1448891
63. Rousseeuw P.J. Croux C. Alternatives to the median absolute deviation J. Am. Stat. Assoc. 1993 88 1273 1283 10.1080/01621459.1993.10476408
64. Devarakonda N. Subhani S. Basha S.A.H. Outliers detection in regression analysis using partial least square approach Proceedings of the ICT and Critical Infrastructure: 48th Annual Convention of Computer Society of India Visakhapatnam, India 13–15 December 2013 Springer Berlin/Heidelberg, Germany 2014 Volume 2 125 135
65. Sklar A. Fonctions de répartition à n dimensions et leurs marges Publ. Inst. Statist. Univ. Paris 1959 8 229 231
66. Klein N. Kneib T. Marra G. Radice R. Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition Flexible Bayesian Regression Modelling Academic Press Cambridge, MA, USA 2020 121 152
67. Li Z. Zhao Y. Botta N. Ionescu C. Hu X. COPOD: Copula-based outlier detection Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM) Sorrento, Italy 17–20 November 2020 IEEE Piscataway, NY, USA 2020 1118 1123
68. Wang Y. Infield D.G. Stephen B. Galloway S.J. Copula-based model for wind turbine power curve outlier rejection Wind Energy 2014 17 1677 1688 10.1002/we.1661
69. Ghalem S.K. Kechar B. Bounceur A. Euler R. A probabilistic multivariate copula-based technique for faulty node diagnosis in wireless sensor networks J. Netw. Comput. Appl. 2019 127 9 25 10.1016/j.jnca.2018.11.009
70. Fang G. Pan R. On multivariate copula modelling of dependent degradation processes Comput. Ind. Eng. 2021 159 107450 10.1016/j.cie.2021.107450
71. Škorić T. Pantelić D. Jelenković B. Bajić D. Noise reduction in two-photon laser scanned microscopic images by singular value decomposition with copula threshold Signal Process. 2022 195 108486 10.1016/j.sigpro.2022.108486
72. Sheikhi A. Amirzadeh V. Mesiar R. A comprehensive family of copulas to model bivariate random noise and perturbation Fuzzy Sets Syst. 2021 415 27 36 10.1016/j.fss.2020.04.010
73. Wang M.L. Lynch J.P. Sohn H. Sensing hardware and data collection methods Sensor Technologies for Civil Infrastructures Woodhead Publishing Sawston, UK 2014 Volume 1
74. Carson E. Cobelli C. Modelling Methodology for Physiology and Medicine 2nd ed. Newnes London, UK 2013
75. Theodoridis S. Bayesian learning: Inference and the EM algorithm Machine Learning Academic Press Cambridge, MA, USA 2020 595 646
76. Haldar S.K. Statistical and geostatistical applications in geology Mineral Exploration Elsevier Amsterdam, The Netherlands 2018 167 194
77. Goldstein M. Dengel A. Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm Proceedings of the 35th German Conference on Artificial Intelligence KI-2012 Saarbrücken, Germany 24–27 September 2012 Volume 1 59 63
78. Latecki L.J. Lazarevic A. Pokrajac D. Outlier detection with kernel density functions Proceedings of the International Workshop on Machine Learning and Data Mining in Pattern Recognition New York, NY, USA 15–20 July 2017 Springer Berlin/Heidelberg, Germany 2007 61 75
79. Schubert E. Zimek A. Kriegel H.P. Generalized outlier detection with flexible kernel density estimates Proceedings of the 2014 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics 2014 Philadelphia, PA, USA 24–26 April 2014 542 550
80. Abdulghafoor S.A. Mohamed L.A. A local density-based outlier detection method for high dimension data Int. J. Nonlinear Anal. Appl. 2022 13 1683 1699
81. Ghoting A. Parthasarathy S. Otey M.E. Fast mining of distance-based outliers in high-dimensional datasets Data Min. Knowl. Discov. 2008 16 349 364 10.1007/s10618-008-0093-2
82. Vu N.H. Gopalkrishnan V. Efficient pruning schemes for distance-based outlier detection Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases Bled, Slovenia 6–10 September 2009 Springer Berlin/Heidelberg, Germany 2009 160 175
83. Navarro J. de Diego I.M. Fernández R.R. Moguerza J.M. Triangle-based outlier detection Pattern Recognit. Lett. 2022 156 152 159 10.1016/j.patrec.2022.03.008
84. Angiulli F. Fassetti F. Uncertain distance-based outlier detection with arbitrarily shaped data objects J. Intell. Inf. Syst. 2021 57 1 24 10.1007/s10844-020-00624-7
85. Román I.S. de Diego I.M. Conde C. Cabello E. Outlier trajectory detection through a context-aware distance Pattern Anal. Appl. 2019 22 831 839 10.1007/s10044-018-0732-1
86. Breunig M.M. Kriegel H.P. Ng R.T. Sander J. LOF: Identifying density-based local outliers Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data Dallas, TX, USA 15–18 May 2000 ACM New York, NY, USA 2000 93 104
87. Bai M. Wang X. Xin J. Wang G. An efficient algorithm for distributed density-based outlier detection on big data Neurocomputing 2016 181 19 28 10.1016/j.neucom.2015.05.135
88. Zhang L. Lin J. Karim R. Adaptive kernel density-based anomaly detection for nonlinear systems Knowl. Based Syst. 2018 139 50 63 10.1016/j.knosys.2017.10.009
89. Pokrajac D. Lazarevic A. Latecki L.J. Incremental local outlier detection for data streams Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining Honolulu, HI, USA 1 March–5 April 2007 IEEE Piscataway, NY, USA 2007 504 515
90. Degirmenci A. Karal O. iMCOD: Incremental multi-class outlier detection model in data streams Knowl. Based Syst. 2022 258 109950 10.1016/j.knosys.2022.109950
91. Gao J. Ji W. Zhang L. Li A. Wang Y. Zhang Z. Cube-based incremental outlier detection for streaming computing Inf. Sci. 2020 517 361 376 10.1016/j.ins.2019.12.060
92. Yan Y. Cao L. Kulhman C. Rundensteiner E. Distributed local outlier detection in big data Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 Halifax, NS, Canada 13–17 August 2017 1225 1234
93. Chen L. Wang W. Yang Y. CELOF: Effective and fast memory efficient local outlier detection in high-dimensional data streams Appl. Soft Comput. 2021 102 107079 10.1016/j.asoc.2021.107079
94. Cassisi C. Ferro A. Giugno R. Pigola G. Pulvirenti A. Enhancing density-based clustering: Parameter reduction and outlier detection Inf. Syst. 2013 38 317 330 10.1016/j.is.2012.09.001
95. Nozad S.A.N. Haeri M.A. Folino G. SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets Knowl. Based Syst. 2021 228 107256 10.1016/j.knosys.2021.107256
96. Ester M. Kriegel H.P. Sander J. Xu X. Density-based spatial clustering of applications with noise Proceedings of the International Conferences Knowledge Discovery and Data Mining 1996 Portland, OR, USA 2–4 August 1996 240
97. Degirmenci A. Karal O. Efficient density and cluster based incremental outlier detection in data streams Inf. Sci. 2022 607 901 920 10.1016/j.ins.2022.06.013
98. Kriegel H.P. Schubert M. Zimek A. Angle-based outlier detection in high-dimensional data Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2008 Las Vegas, NV, USA 24–27 August 2008 444 452
99. Al-taei R. Haeri M.A. An ensemble angle-based outlier detection for big data Proceedings of the International Congress on High-Performance Computing and Big Data Analysis Tehran, Iran 23–25 April 2019 Springer Cham, Switzerland 2019 98 108
100. Ye H. Kitagawa H. Xiao J. Continuous angle-based outlier detection on high-dimensional data streams Proceedings of the 19th International Database Engineering & Applications Symposium Yokohama, Japan 13–15 July 2015 162 167
101. Thordsen E. Schubert E. ABID: Angle based intrinsic dimensionality Proceedings of the International Conference on Similarity Search and Applications Copenhagen, Denmark 30 September–2 October 2020 Springer Cham, Switzerland 2020 218 232
102. Cortes C. Vapnik V. Support-vector networks Mach. Learn. 1995 20 273 297 10.1007/BF00994018
103. Urso A. Fiannaca A. La Rosa M. Ravì V. Rizzo R. Data mining: Classification and prediction Encycl. Bioinform. Comput. Biol. ABC Bioinform. 2018 1 384
104. Singh P.K. Gupta S. Vashistha R. Nandi S.K. Nandi S. Machine learning based approach to detect position falsification attack in VANETs Proceedings of the Security and Privacy: 2nd ISEA International Conference, ISEA-ISAP 2018 Jaipur, India 9–11 January 2019 Springer Singapore 2019 166 178
105. Parras J. Zazo S. Using one class SVM to counter intelligent attacks against an SPRT defense mechanism Ad Hoc Netw. 2019 94 101946 10.1016/j.adhoc.2019.101946
106. Sumathy S. Revathy M. Manikandan R. Improving the state of materials in cybersecurity attack detection in 5G wireless systems using machine learning Mater. Today Proc. 2023 81 700 707 10.1016/j.matpr.2021.04.171
107. Erfani S.M. Rajasegarar S. Karunasekera S. Leckie C. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning Pattern Recognit. 2016 58 121 134 10.1016/j.patcog.2016.03.028
108. Zhou X. Zhang X. Wang B. Online support vector machine: A survey Proceedings of the Harmony Search Algorithm 2nd International Conference on Harmony Search Algorithm (ICHSA2015) Seoul, Republic of Korea 19–21 August 2015 Springer Berlin/Heidelberg, Germany 2016 269 278
109. Martín L. Sánchez L. Lanza J. Sotres P. Development and evaluation of Artificial Intelligence techniques for IoT data quality assessment and curation Internet Things 2023 22 100779 10.1016/j.iot.2023.100779
110. Rosenblatt F. The Perceptron, a Perceiving and Recognizing Automaton (Project PARA) Cornell Aeronautical Laboratory Buffalo, NY, USA 1957
111. Krishnan S. Machine learning for biomedical signal analysis Biomedical Signal Analysis for Connected Healthcare Elsevier Amsterdam, The Netherlands 2021 223 264
112. Al-Jabery K. Obafemi-Ajayi T. Olbricht G. Wunsch D. Computational Learning Approaches to Data Analytics in Biomedical Applications Academic Press Cambridge, MA, USA 2019
113. Svozil D. Kvasnicka V. Pospichal J. Introduction to multi-layer feed-forward neural networks Chemom. Intell. Lab. Syst. 1997 39 43 62 10.1016/S0169-7439(97)00061-0
114. Iqbal A. Aftab S. A feed-forward and pattern recognition ANN model for network intrusion detection Int. J. Comput. Netw. Inf. Secur. 2019 11 19 10.5815/ijcnis.2019.04.03
115. Ullah I. Mahmoud Q.H. An anomaly detection model for IoT networks based on flow and flag features using a feed-forward neural network Proceedings of the 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Las Vegas, NV, USA 8–11 January 2022 IEEE Piscataway, NY, USA 2022 363 368
116. Li H. Wang X. Yang Z. Ali S. Tong N. Baseer S. Correlation-Based Anomaly Detection Method for Multi-sensor System Comput. Intell. Neurosci. 2022 2022 4756480 10.1155/2022/4756480
117. Kang Z. Yang B. Nielsen M. Deng L. Yang S. A buffered online transfer learning algorithm with multi-layer network Neurocomputing 2022 488 581 597 10.1016/j.neucom.2021.11.066
118. Elman J.L. Finding structure in time Cogn. Sci. 1990 14 179 211 10.1207/s15516709cog1402_1
119. DiPietro R. Hager G.D. Deep learning: RNNs and LSTM Handbook of Medical Image Computing and Computer Assisted Intervention Academic Press Cambridge, MA, USA 2020 503 519
120. Singh E. Kuzhagaliyeva N. Sarathy S.M. Using deep learning to diagnose preignition in turbocharged spark-ignited engines Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines Elsevier Amsterdam, The Netherlands 2022 213 237
121. Gupta T.K. Raza K. Optimization of ANN architecture: A review on nature-inspired techniques Machine Learning in Bio-Signal Analysis and Diagnostic Imaging Academic Press Cambridge, MA, USA 2019 159 182
122. Zhu R. Tu X. Huang J.X. Deep learning on information retrieval and its applications Deep Learning for Data Analytics Academic Press Cambridge, MA, USA 2020 125 153
123. Muharemi F. Logofătu D. Leon F. Machine learning approaches for anomaly detection of water quality on a real-world data set J. Inf. Telecommun. 2019 3 294 307 10.1080/24751839.2019.1565653
124. Ackerson J.M. Dave R. Seliya N. Applications of recurrent neural network for biometric authentication & anomaly detection Information 2021 12 272 10.3390/info12070272
125. Jeong S. Ferguson M. Law K.H. Sensor data reconstruction and anomaly detection using bidirectional recurrent neural network Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019 Denver, CO, USA 3–7 March 2019 Volume 10970 157 167
126. Hochreiter S. Schmidhuber J. Long short-term memory Neural Comput. 1997 9 1735 1780 10.1162/neco.1997.9.8.1735 9377276
127. Ankit U. Transformer Neural Network: Step-By-Step Breakdown of the Beast 2020 Available online: https://towardsdatascience.com/transformer-neural-network-step-by-step-breakdown-of-the-beast-b3e096dc857f (accessed on 20 September 2023)
128. Al Mamun S.A. Beyaz M. LSTM Recurrent Neural Network (RNN) for Anomaly Detection in Cellular Mobile Networks Proceedings of the Machine Learning for Networking: First International Conference MLN 2018 Paris, France 27–29 November 2018 222 237
129. Muhuri P.S. Chatterjee P. Yuan X. Roy K. Esterline A. Using a long short-term memory recurrent neural network (LSTM-RNN) to classify network attacks Information 2020 11 243 10.3390/info11050243
130. Sagheer A. Hamdoun H. Youness H. Deep LSTM-based transfer learning approach for coherent forecasts in hierarchical time series Sensors 2021 21 4379 10.3390/s21134379 34206750
131. Bleiweiss A. LSTM Neural Networks for Transfer Learning in Online Moderation of Abuse Context Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019) Prague, Czech Republic 19–21 February 2019 112 122
132. Negi N. Jelassi O. Chaouchi H. Clemençon S. Distributed online Data Anomaly Detection for connected vehicles Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Fukuoka, Japan 19–21 February 2020 IEEE Piscataway, NY, USA 2020 616 621
133. Raj P. Evangeline P. The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases Academic Press Cambridge, MA, USA 2020
134. Pavithra V. Jayalakshmi V. Smart energy and electric power system: Current trends and new intelligent perspectives and introduction to AI and power system Smart Energy and Electric Power Systems Elsevier Amsterdam, The Netherlands 2023 19 36
135. Hung C.L. Deep learning in biomedical informatics Intelligent Nanotechnology Elsevier Amsterdam, The Netherlands 2023 307 329
136. Teuwen J. Moriakov N. Convolutional neural networks Handbook of Medical Image Computing and Computer Assisted Intervention Academic Press Cambridge, MA, USA 2020 481 501
137. Jeon W. Ko G. Lee J. Lee H. Ha D. Ro W.W. Deep learning with GPUs Adv. Comput. 2021 122 167 215
138. Mishra S. Tripathy H.K. Mallick P.K. Sangaiah A.K. Chae G.S. Cognitive Big Data Intelligence with a Metaheuristic Approach Academic Press Cambridge, MA, USA 2021
139. Hinton G.E. Osindero S. Teh Y.W. A fast learning algorithm for deep belief nets Neural Comput. 2006 18 1527 1554 10.1162/neco.2006.18.7.1527
140. Mocanu E. Nguyen P.H. Gibescu M. Deep learning for power system data analysis Big Data Application in Power Systems Elsevier Amsterdam, The Netherlands 2018 125 158
141. Liu H. Wind Forecasting in Railway Engineering Elsevier Amsterdam, The Netherlands 2021
142. Talapula D.K. Kumar A. Ravulakollu K.K. Kumar M. Anomaly Detection in Online Data Streams Using Deep Belief Neural Networks Proceedings of the Doctoral Symposium on Computational Intelligence Lucknow, India 3 March 2023 Springer Singapore 2023 729 749
143. Goodfellow I.J. Pouget-Abadie J. Mirza M. Xu B. Warde-Farley D. Ozair S. Courville A. Bengio Y. Generative adversarial nets Proceedings of the 27th International Conference on Neural Information Processing Systems NIPS’14 Montreal, QC, Canada 8–13 December 2014 Volume 2 2672 2680
144. Goodfellow I. Pouget-Abadie J. Mirza M. Xu B. Warde-Farley D. Ozair S. Courville A. Bengio Y. Generative adversarial networks Commun. ACM 2020 63 139 144 10.1145/3422622
145. Aggarwal A. Mittal M. Battineni G. Generative adversarial network: An overview of theory and applications Int. J. Inf. Manag. Data Insights 2021 1 100004 10.1016/j.jjimei.2020.100004
146. Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A.N. Kaiser Ł. Polosukhin I. Attention is All You Need Proceedings of the Advances in Neural Information Processing Systems NeurIPS 2017 Long Beach, CA, USA 4–9 December 2017 5998 6008
147. Li G. Duan Z. Liang L. Zhu H. Hu A. Cui Q. Chen B. Hu W. Outlier data mining method considering the output distribution characteristics for photovoltaic arrays and its application Energy Rep. 2020 6 2345 2357 10.1016/j.egyr.2020.08.034
148. Srinu S. Mishra A.K. Efficient elimination of erroneous nodes in cooperative sensing for cognitive radio networks Comput. Electr. Eng. 2016 52 284 292 10.1016/j.compeleceng.2015.05.004
149. Zhao Y. Lehman B. Ball R. Mosesian J. de Palma J.F. Outlier detection rules for fault detection in solar photovoltaic arrays Proceedings of the 2013 28th Annual IEEE Applied Power Electronics Conference and Exposition (APEC) Long Beach, CA, USA 17–21 March 2013 IEEE Piscataway, NY, USA 2013 2913 2920
150. Schlechtingen M. Santos I.F. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection Mech. Syst. Signal Process. 2011 25 1849 1875 10.1016/j.ymssp.2010.12.007
151. Leigh C. Alsibai O. Hyndman R.J. Kandanaarachchi S. King O.C. McGree J.M. Neelamraju C. Strauss J. Talagala P.D. Turner R.D. et al. A framework for automated anomaly detection in high frequency water-quality data from in situ sensors Sci. Total Environ. 2019 664 885 898 10.1016/j.scitotenv.2019.02.085
152. Owolabi O. Okoh D. Rabiu B. Obafaye A. Dauda K. A median absolute deviation-neural network (MAD-NN) method for atmospheric temperature data cleaning MethodsX 2021 8 101533 10.1016/j.mex.2021.101533 34754802
153. Bae I. Ji U. Application of Outlier Detection and Smoothing Algorithm for Monitoring Water Level and Discharge by Ultrasonic Sensor Proceedings of the AGU Fall Meeting Abstracts San Francisco, CA, USA 9–13 December 2019 Volume 2019 H53K-1913
154. Belkhouche F. Robust calibration of MEMS accelerometers in the presence of outliers IEEE Sens. J. 2022 22 9500 9508 10.1109/JSEN.2022.3163964
155. Diaz-Rozo J. Bielza C. Larrañaga P. Clustering of data streams with dynamic Gaussian mixture models: An IoT application in industrial processes IEEE Internet Things J. 2018 5 3533 3547 10.1109/JIOT.2018.2840129
156. Reddy A. Ordway-West M. Lee M. Dugan M. Whitney J. Kahana R. Ford B. Muedsam J. Henslee A. Rao M. Using gaussian mixture models to detect outliers in seasonal univariate network traffic Proceedings of the 2017 IEEE Security and Privacy Workshops (SPW) San Jose, CA, USA 25 May 2017 IEEE Piscataway, NY, USA 2017 229 234
157. Kalaycı İ. Ercan T. Anomaly detection in wireless sensor networks data by using histogram based outlier score method Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) Ankara, Turkey 19–21 October 2018 IEEE Piscataway, NY, USA 2018 1 6
158. Çakmakçı S.D. Kemmerich T. Ahmed T. Baykal N. Online DDoS attack detection using Mahalanobis distance and Kernel-based learning algorithm J. Netw. Comput. Appl. 2020 168 102756 10.1016/j.jnca.2020.102756
159. Saeed M.M. A real-time adaptive network intrusion detection for streaming data: A hybrid approach Neural Comput. Appl. 2022 34 6227 6240 10.1007/s00521-021-06786-x
160. Alamaniotis M. Fuzzy Integration of kernel-based Gaussian Processes applied to Anomaly Detection in Nuclear Security 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA), Chania Crete, Greece, 12–14 July 2021 IEEE Piscataway, NY, USA 2021 1 4
161. Bhattacharjee S. Marchang N. Malicious user detection with local outlier factor during spectrum sensing in cognitive radio network Int. J. Ad Hoc Ubiquitous Comput. 2019 30 215 223 10.1504/IJAHUC.2019.098865
162. Chhetry B. Marchang N. Detection of primary user emulation attack (PUEA) in cognitive radio networks using one-class classification arXiv 2021 2106.10964
163. Baek S. Kwon D. Suh S.C. Kim H. Kim I. Kim J. Clustering-based label estimation for network anomaly detection Digit. Commun. Netw. 2021 7 37 44 10.1016/j.dcan.2020.06.001
164. Premkumar M. Ashokkumar S.R. Jeevanantham V. Mohanbabu G. AnuPallavi S. Scalable and energy efficient cluster based anomaly detection against denial of service attacks in wireless sensor networks Wirel. Pers. Commun. 2023 129 2669 2691 10.1007/s11277-023-10252-3
165. Yang L. Lu Y. Yang S.X. Guo T. Liang Z. A secure clustering protocol with fuzzy trust evaluation and outlier detection for industrial wireless sensor networks IEEE Trans. Ind. Inform. 2020 17 4837 4847 10.1109/TII.2020.3019286
166. Jha H.S. Khanal A. Seikh H.M.D. Lee W.J. A comparative study on outlier detection techniques for noisy production data from unconventional shale reservoirs J. Nat. Gas Sci. Eng. 2022 105 104720 10.1016/j.jngse.2022.104720
167. Soumya T.R. Revathy S. A Novel Approach for Cyber Threat Detection Based on Angle-Based Subspace Anomaly Detection Cybern. Syst. 2022 1 10 10.1080/01969722.2022.2148509
168. Vanitha N. Ganapathi P. Traffic analysis of UAV networks using enhanced deep feed forward neural networks (EDFFNN) Handbook of Research on Machine and Deep Learning Applications for Cyber Security IGI Global Hershey, PA, USA 2020 219 244
169. Reddy D.K. Behera H.S. Nayak J. Vijayakumar P. Naik B. Singh P.K. Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities Trans. Emerg. Telecommun. Technol. 2021 32 4121 10.1002/ett.4121
170. Yu Y. Wu X. Yuan S. Anomaly detection for internet of things based on compressed sensing and online extreme learning machine autoencoder J. Phys. Conf. Ser. 2020 1544 012027 10.1088/1742-6596/1544/1/012027
171. Adkisson M. Kimmell J.C. Gupta M. Abdelsalam M. Autoencoder-based anomaly detection in smart farming ecosystem Proceedings of the 2021 IEEE International Conference on Big Data (Big Data) Orlando, FL, USA 15–18 December 2021 IEEE Piscataway, NY, USA 2021 3390 3399
172. Han P. Ellefsen A.L. Li G. Holmeset F.T. Zhang H. Fault detection with LSTM-based variational autoencoder for maritime components IEEE Sens. J. 2021 21 21903 21912 10.1109/JSEN.2021.3105226
173. Alabadi M. Celik Y. Detection for cyber-security based on convolution neural network: A survey Proceedings of the 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Ankara, Turkey 26–28 June 2020 IEEE Piscataway, NY, USA 2020 1 14
174. Sun H. Chen M. Weng J. Liu Z. Geng G. Anomaly detection for in-vehicle network using CNN-LSTM with attention mechanism IEEE Trans. Veh. Technol. 2021 70 10880 10893 10.1109/TVT.2021.3106940
175. Tschuchnig M.E. Gadermayr M. Anomaly detection in medical imaging-a mini review Data Science–Analytics and Applications: Proceedings of the 4th International Data Science Conference—iDSC2021, Online, 16–18 October 2021 Springer Wiesbaden, Germany 2022 33 38
176. Arabahmadi M. Farahbakhsh R. Rezazadeh J. Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging Sensors 2022 22 1960 10.3390/s22051960
177. Qiao Y. Cui X. Jin P. Zhang W. Fast outlier detection for high-dimensional data of wireless sensor networks Int. J. Distrib. Sens. Netw. 2020 16 1550147720963835 10.1177/1550147720963835
178. Sarkar N. Keserwani P.K. Govil M.C. A better and fast cloud intrusion detection system using improved squirrel search algorithm and modified deep belief network Clust. Comput. 2023 27 1699 1718 10.1007/s10586-023-04037-3
179. Deecke L. Vandermeulen R. Ruff L. Mandt S. Kloft M. Image anomaly detection with generative adversarial networks Proceedings of the Machine Learning and Knowledge Discovery in Databases: European Conference ECML PKDD 2018 Dublin, Ireland 10–14 September 2018 Springer Berlin/Heidelberg, Germany 2018 3 17
180. Jiang T. Li Y. Xie W. Du Q. Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection IEEE Trans. Geosci. Remote Sens. 2020 58 4666 4679 10.1109/TGRS.2020.2965961
181. Jin P. Mou L. Xia G.S. Zhu X.X. Anomaly detection in aerial videos with transformers IEEE Trans. Geosci. Remote Sens. 2022 60 5628213 10.1109/TGRS.2022.3198130
182. Chen Z. Chen D. Zhang X. Yuan Z. Cheng X. Learning graph structures with transformer for multivariate time-series anomaly detection in IoT IEEE Internet Things J. 2021 9 9179 9189 10.1109/JIOT.2021.3100509
183. Zhang S. Liu Y. Zhang X. Cheng W. Chen H. Xiong H. Cat: Beyond efficient transformer for content-aware anomaly detection in event sequences Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Washington, DC, USA 14–18 August 2022 4541 4550
184. Zhang J. Zhao H. Li J. TRS: Transformers for remote sensing scene classification Remote Sens. 2021 13 4143 10.3390/rs13204143
185. ODDS Outliers Detection Datasets Available online: https://odds.cs.stonybrook.edu/ (accessed on 23 July 2024)
186. IEEE Dataport. IEEE Dataport Datasets Available online: https://ieee-dataport.org/datasets (accessed on 23 July 2024)
187. University of California Irving University of California Irving Database Available online: https://kdd.ics.uci.edu/databases/ (accessed on 23 July 2024)
188. UCI Machine Learning Repository. KDD Cup 1999 Data. University of California, Irvine 1999 Available online: https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (accessed on 29 July 2024)
189. University of New Brunswick NSL-KDD Dataset Information Security Centre of Excellence, University of New Brunswick. 2009 Available online: http://www.unb.ca/cic/datasets/nsl.html (accessed on 29 July 2024)
190. Koppula M. Joseph L. A Real Time Dataset IDSIoT 2024 IEEE Data Port. 2024 10.21227/gfaz-t124
191. Pack M.L. Corel Histogram Dataset Available online: https://www.mlpack.org/datasets/ (accessed on 23 July 2024)
192. Pahl M.O. Aubet F.X. All Eyes on You: Distributed Multi-Dimensional IoT Microservice Anomaly Detection Proceedings of the 2018 14th International Conference on Network and Service Management (CNSM) Rome, Italy 5–9 November 2018 IEEE Piscataway, NY, USA 2018 72 80
193. Intel Berkeley Research Lab Intel Berkeley Research Lab Sensor Data Intel Corporation. 2004 Available online: http://db.csail.mit.edu/labdata/labdata.html (accessed on 29 July 2024)
194. Zhang Y. Zhu Y. Nichols E. Wang Q. Zhang S. Smith C. Howard S. A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images arXiv 2018 1812.10366
195. Jorge R.-O. Davide A. Alessandro G. Luca O. Xavier P. Human Activity Recognition Using Smartphones UCI Machine Learning Repository University of California Irvine, CA, USA 2012 10.24432/C54S4K
196. Billur B. Kerem A. Daily and Sports Activities UCI Machine Learning Repository University of California Irvine, CA, USA 2013 10.24432/C5C59F
197. Center for Atmospheric Research Tropospheric Data Acquisition Network (TRODAN) Data 2013 Available online: https://carnasrda.com/trodan_data (accessed on 29 July 2024)
198. Canadian Institute for Cybersecurity CICIDS2017 Dataset University of New Brunswick. 2017 Available online: https://www.unb.ca/cic/datasets/ids-2017.html (accessed on 29 July 2024)
199. University of New South Wales BoT-IoT Dataset UNSW Canberra Cyber. 2018 Available online: https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/ (accessed on 29 July 2024)
200. University of California Merced. UC Merced Land Use Dataset 2010 Available online: http://weegee.vision.ucmerced.edu/datasets/landuse.html (accessed on 29 July 2024)
201. Xia G.-S. AID: Aerial Image Dataset. Wuhan University 2017 Available online: https://captain-whu.github.io/DiRS/ (accessed on 29 July 2024)
202. Haikel H. NWPU-RESISC45 Dataset with 12 Classes Figshare London, UK 2021 10.6084/m9.figshare.16674166.v1
203. Wang Q. Liu S. Chanussot J. Li X. Scene classification with recurrent attention of VHR remote sensing images IEEE Trans. Geosci. Remote Sens. 2018 57 1155 1167 10.1109/TGRS.2018.2864987

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