References
- Liu FT, Ting KM, Zhou ZH. Isolation forest. In: 2008 eighth IEEE international conference on data mining. IEEE; 2008. pp. 413–422. doi:10.1109/ICDM.2008.17
- Kumar N, Kumar U. Anomaly-based network intrusion detection: an outlier detection techniques. In: Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). Springer; 2018. pp. 262–269. doi:10.1007/978-3-319-60618-7 26.
- Tripathi D, Edla DR, Kuppili V, et al. Credit scoring model based on weighted voting and cluster based feature selection. Procedia Comput Sci. 2018;132:22–31. doi:10.1016/j.procs.2018.05.055
- Dovoedo Y, Chakraborti S. Boxplot-based outlier detection for the location-scale family. Commun Stat B: Simul Comput. 2015;44:1492–1513. doi:10.1080/03610918.2013.813037
- Liu F, Yu Y, Song P, et al. Scalable kde-based top-n local outlier detection over large-scale data streams. Knowl Based Syst. 2020;204:106186. doi:10.1016/j.knosys.2020.106186
- Breunig MM, Kriegel HP, Ng RT, Sander J. Lof: iden-tifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data; 2000. pp. 93–104. doi:10.1145/342009.335388
- Huang JW, Zhong MX, Jaysawal BP. Tadilof: time aware density-based incremental local outlier detection in data streams. Sensors. 2020;20:5829. doi:10.3390/s20205829
- Jin W, Tung AK, Han J, Wang W. Ranking outliers using symmetric neighborhood relationship. In: Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9- 12, 2006. Proceedings 10. Springer; 2006. pp. 577–593. doi:10.1007/11731139
- Karale A, Lazarova M, Koleva P, Poulkov V. A hybrid pso-milof approach for outlier detection in streaming data. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). IEEE; 2020. pp. 474–479. doi:10.1109/TSP49548.2020.9163430
- Radovanovic M, Nanopoulos A, Ivanovic M. Reverse near-est neighbors in unsupervised distance-based outlier detection. IEEE Trans Knowl Data Eng. 2014;27:1369–1382. doi:10.1109/TKDE.2014.2365790
- Lin WC, Tsai CF, Hu YH, et al. Clustering-based un-dersampling in class-imbalanced data. Inf Sci (Ny). 2017;409:17–26. doi:10.1016/j.ins.2017.05.008
- Palma AT, Bogorny V, Kuijpers B, Alvares LO. A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing; 2008. pp. 863–868. doi:10.1145/1363686.1363886
- Liu FT, Ting KM, Zhou ZH. Isolation-based anomaly detection. ACM Trans Knowl Discov Data. 2012;6:1–39. doi:10.1145/2133360.2133363
- Zhang X, Dou W, He Q, et al. Lshiforest: A generic framework for fast tree isolation based ensem- ble anomaly analysis. In: 2017 IEEE 33rd international conference on data engineering (ICDE). IEEE; 2017. pp. 983–994. doi:10.1109/ICDE.2017.145
- Iqbal T, Qureshi S. Reconstruction probability-based anomaly detec-tion using variational auto-encoders. Int J Comput Appl. 2023;45:231–237. doi:10.1080/1206212X.2022.2143026
- Swaroop CR, Raja K. At-densenet with salp swarm optimization for outlier prediction. Int J Comput Appl. 2023;45:735–747. doi:10.1080/1206212X.2023.2273015
- Alghushairy O, Alsini R, Soule T, et al. A review of local outlier factor algorithms for outlier detection in big data streams. Big Data Cogn Comput. 2020;5:1. doi:10.3390/bdcc5010001
- Chen L, Gao S, Cao X. Research on real-time outlier detection over big data streams. Int J Comput Appl. 2020;42:93–101. doi:10.1080/1206212X.2017.1397388
- Zimek A, Schubert E, Kriegel HP. A survey on unsupervised outlier detection in high-dimensional numerical data. Stat Anal Data Min. 2012;5:363–387. doi:10.1002/sam.11161
- Poggio T, Mhaskar H, Rosasco L, et al. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review. Int J Autom Comput. 2017;14:503–519. doi:10.1007/s11633-017-1054-2
- Tang J, Chen Z, Fu AWC, Cheung DW. Enhancing effective ness of outlier detections for low density patterns. In: Advances in Knowl-edge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6–8, 2002 Proceedings 6. Springer; 2002. pp. 535–548. doi:10.1007/3-540-47887-653
- He Z, Xu X, Deng S. Discovering cluster-based local outliers. Pattern Recognit Lett. 2003;24:1641–1650. doi:10.1016/S0167-8655(03)00003-5
- Papadimitriou S, Kitagawa H, Gibbons PB, Faloutsos C. Loci: Fast outlier detection using the local correlation integral. In: Proceedings 19th international conference on data engineering (Cat. No. 03CH37405). IEEE; 2003. pp. 315–326. doi:10.1109/ICDE.2003.1260802
- Kriegel HP, Schubert M, Zimek A. Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining; 2008. pp. 444–452. doi:10.1145/1401890.1401946
- Zhang K, Hutter M, Jin H. A new local distance-based outlier de-tection approach for scattered real-world data. In: Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings 13. Springer; 2009. pp. 813–822. doi:10.1007/978-3-642-01307-284
- Kriegel HP, Kroger P, Schubert E, Zimek A. Loop: local outlier probabilities. In: Proceedings of the 18th ACM conference on Information and knowledge management; 2009. pp. 1649–1652. doi:10.1145/1645953.1646195
- Rayana S, Zhong W, Akoglu L. Sequential ensemble learning for outlier detection: A bias-variance perspective. In: 2016 IEEE 16th international conference on data mining (ICDM). IEEE; 2016. pp. 1167–1172. doi:10.1109/ICDM.2016.0154
- Bai M, Wang X, Xin J, et al. An efficient algorithm for dis-tributed density-based outlier detection on big data. Neurocomputing. 2016;181:19–28. doi:10.1016/j.neucom.2015.05.135
- Pokrajac D, Lazarevic A, Latecki LJ. Incremental lo-cal outlier detection for data streams. In: 2007 IEEE symposium on computational intelligence and data mining. IEEE; 2007. pp. 504–515. doi:10.1109/CIDM.2007.368917
- Pokrajac D, Reljin N, Pejcic N, Lazarevic A. Incremen-tal connectivity-based outlier factor algorithm. In: Visions of Com- puter Science-BCS International Academic Conference; 2008. pp. 211–223. doi:10.14236/ewic/VOCS2008.18
- Lu X, Yang T, Liao Z, et al. Incremental outlier detection in data streams using local correlation integral. In: Proceedings of the 2009 ACM symposium on Applied Computing; 2009. pp. 1520–1521. doi:10.1145/1529282.1529623
- Gao K, Shao FJ, Sun RC . n-inclof: A dynamic local outlier detection algorithm for data streams. In: 2010 2nd International Conference on Signal Processing Systems. IEEE; 2010. pp. V2–179. doi:10.1109/ICSPS.2010.5555276
- Karimian SH, Kelarestaghi M, Hashemi S. I-inclof: improved in-cremental local outlier detection for data streams. In: The 16th CSI Inter- national Symposium on Artificial Intelligence and Signal Processing (AISP 2012). IEEE; 2012. pp. 023–028. doi:10.1109/AISP.2012.6313711
- Hamlet C, Straub J, Russell M, et al. An incremental and approximate local outlier probability algorithm for intrusion detec- tion and its evaluation. J Cyber Secur Technol. 2017;1:75–87. doi:10.1080/23742917.2016.1226651
- Ye H, Kitagawa H, Xiao J. Continuous angle-based outlier detec-tion on high-dimensional data streams. In: Proceedings of the 19th Inter- national Database Engineering & Applications Symposium; 2015. pp. 162–167. doi:10.1145/2790755.2790775
- Goodge A, Hooi B, Ng SK, Ng WS. Lunar: Unifying local outlier detection methods via graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence; 2022. pp. 6737–6745. doi:10.1609/aaai.v36i6.20629
- Mao Y, Tian L, Wang Y, et al. Fast outlier detection algorithm in data stream with local density of vector dot product. Comput Eng. 2020;46:132–138. doi:10.19678/j.issn.1000-3428.0056453
- Shou Z, Zou F, Tian H, Li S. Outlier detection based on local density of vector dot product in data stream. In: Security with Intelligent Computing and Big-data Services: Proceedings of the Second International Conference on Security with Intelligent Computing and Big Data Services (SICBS-2018) 2. Springer; 2020. pp. 170–184. doi:10.1007/978-3-030-16946-614.
- Jin C, Yi K, Chen L, et al. Sliding-window top-k queries on uncertain streams. Proc VLDB Endow. 2008;1:301–312. doi:10.14778/1453856.1453892