In the process of analysing data, outlier detection (i.e., anomaly detection or novelty identification) is often misinterpreted
to an identification of rare observations, occurrence or an item, which deviates highly from enormous data and never conforms to well-
defined ideologies of a normal behaviour. The samples could stimulate more suspicion of being produced from various techniques, or
appear unpredictable with the remaining portion of the specific dataset. Anomaly detection draws application in different domains such
as neuroscience, statistics, machine vision, medicine, financial fraud, law enforcement and cyber security. The data that has been
collected from real-life applications are rapidly increasing in dimension and size. As the aspect of dimensionality keeps increasing, data
items become significantly sparse, amounting to an identification of variances becoming problematic. In addition, more conventional
approaches for anomaly detection cannot function in a proper manner. In this paper, we have evaluated the applications and methods of
anomaly detection.
P. Panagiotou, N. Mengidis, T. Tsikrika, S. Vrochidis and I. Kompatsiaris, "Host-based Intrusion Detection Using Signature-based and AI-
driven Anomaly Detection Methods", Information & Security: An International Journal, vol. 50, pp. 37-48, 2021. Doi: 10.11610/isij.5016.
D. Bouchaffra, M. Cheriet, P. Jodoin and D. Beck, "Machine learning and pattern recognition models in change detection", Pattern Recognition,
vol. 48, no. 3, pp. 613-615, 2015. Doi: 10.1016/j.patcog.2014.10.019.
K. SARAVANAN, "An Efficient Detection Mechanism for Intrusion Detection Systems Using Rule Learning Method", International Journal
of Computer and Electrical Engineering, pp. 503-506, 2009. Doi: 10.7763/ijcee.2009.v1.76.
A. Paulino, "The promise of intensity modulated radiation therapy", Pediatric Blood & Cancer, vol. 63, no. 9, pp. 1513-1514, 2016. Doi:
10.1002/pbc.26081.
N. Singh, D. Virmani and X. Gao, "A Fuzzy Logic-Based Method to Avert Intrusions in Wireless Sensor Networks Using WSN-DS Dataset",
International Journal of Computational Intelligence and Applications, vol. 19, no. 03, p. 2050018, 2020. Doi: 10.1142/s1469026820500182.
Z. XIE, Y. XIN and J. YANG, "Multi-Batch Processing Integrated Scheduling Algorithm Based on Signal Driven", Chinese Journal of
Computers, vol. 36, no. 4, pp. 818-828, 2014. Doi: 10.3724/sp.j.1016.2013.00818.
R. Rosillo, J. Giner and D. De la Fuente, "Stock Market Simulation Using Support Vector Machines", Journal of Forecasting, vol. 33, no. 6, pp.
488-500, 2014. Doi: 10.1002/for.2302.
R. Heller, Y. Heller and M. Gorfine, "A consistent multivariate test of association based on ranks of distances", Biometrika, vol. 100, no. 2, pp.
503-510, 2012. Doi: 10.1093/biomet/ass070.
K. Adhinugraha, D. Taniar and M. Indrawan, "Finding reverse nearest neighbors by region", Concurrency and Computation: Practice and
Experience, vol. 26, no. 5, pp. 1142-1156, 2013. Doi: 10.1002/cpe.3056.
M. Alvin, K. Adhinugraha, S. Alamri and U. Mir, "Influence zone expansion for reverse k nearest neighbours query", Multimedia Tools and
Applications, 2021. Available: 10.1007/s11042-021-11275-3.
"Improved CURE Clustering Algorithm using Shared Nearest Neighbour Technique", International Journal of Emerging Trends in Engineering
Research, vol. 9, no. 2, pp. 151-157, 2021. Available: 10.30534/ijeter/2021/20922021.
A. Sarwar, "K-Nearest Neighbours based diagnosis of hyperglycemia", International Journal of Trend in Scientific Research and Development,
vol. -2, no. -1, pp. 611-614, 2017. Available: 10.31142/ijtsrd7046.
I. Etikan, "Combination of Probability Random Sampling Method with Non Probability Random Sampling Method (Sampling Versus Sampling
Methods)", Biometrics & Biostatistics International Journal, vol. 5, no. 6, 2017. Available: 10.15406/bbij.2017.05.00148.
G. Liu, K. Sim, J. Li and L. Wong, "Efficient mining of distance-based subspace clusters", Statistical Analysis and Data Mining: The ASA Data
Science Journal, vol. 2, no. 5-6, pp. 427-444, 2009. Available: 10.1002/sam.10062.
G. Li, H. Meng, W. Lu, J. Yang and M. Yang, "Asymmetric bagging and feature selection for activities prediction of drug molecules", BMC
Bioinformatics, vol. 9, no. 6, 2008. Available: 10.1186/1471-2105-9-s6-s7.
J. Dib, K. Sirlantzis and G. Howells, "A Review on Negative Road Anomaly Detection Methods", IEEE Access, vol. 8, pp. 57298-57316, 2020.
Available: 10.1109/access.2020.2982220.
G. Corach and A. Maestripieri, "Redundant decompositions, angles between subspaces and oblique projections", Publicacions Matemàtiques,
vol. 54, pp. 461-484, 2010. Available: 10.5565/publmat_54210_09.
D. Lazoff and A. Sherman, "Expected Wire Length between Two Randomly Chosen Terminals", SIAM Review, vol. 37, no. 2, pp. 235-235,
1995. Available: 10.1137/1037047.
P. Ranjan, D. Bingham and A. Dean, "Existence and construction of randomization defining contrast subspaces for regular factorial designs",
The Annals of Statistics, vol. 37, no. 6, 2009. Doi: 10.1214/08-aos644.
S. Gupta, "Frequent Item-Set Mining and Clustering Based Ranked Biomedical Text Summarization", SSRN Electronic Journal, 2022. Doi:
10.2139/ssrn.4067265.
S. Yi, Z. Fan and D. Wu, "Batch feature standardization network with triplet loss for weakly-supervised video anomaly detection", Image and
Vision Computing, vol. 120, p. 104397, 2022. Doi: 10.1016/j.imavis.2022.104397.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Sidny Chalhoub, “A Critical Review of the Applications and AI Techniques for Anomaly Detection", vol.2, no.3, pp. 098-109, July 2022. doi: 10.53759/181X/JCNS202202013.
