The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Machine Learning Techniques for Anomaly Detection: An Overview
Open accessAbstract
Intrusion detection has gain a broad attention and become a fertile field for several researches, and still being the subject of widespread interest by researchers.The intrusion detection community still confronts difficult problems even after many years of research.Reducing the large number of false alerts during the process of detecting unknown attack patterns remains unresolved problem.However, several research results recently have shown that there are potential solutions to this problem.Anomaly detection is a key issue of intrusion detection in which perturbations of normal behavior indicates a presence of intended or unintended induced attacks, faults, defects and others.This paper presents an overview of research directions for applying supervised and unsupervised methods for managing the problem of anomaly detection.The references cited will cover the major theoretical issues, guiding the researcher in interesting research directions.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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Neural networks for pattern recognition
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Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,199
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
Dunn, J. C. (1973) · Journal of Cybernetics · doi:10.1080/01969727308546046 · Cited by 5,049
Maximum Likelihood from Incomplete Data via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1976) · Cited by 4,517
Learning internal representations by error propagation
RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002
Cited by
Anomaly detection of defects on concrete structures with the convolutional autoencoder
Chow, J. K., Su, Z., Wu, J., Tan, P. S., Mao, X., & Wang, Y. H. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101105 · Cited by 336
A systematic review of data science and machine learning applications to the oil and gas industry
Tariq, Z., Aljawad, M. S., Hasan, A., Murtaza, M., Mohammed, E., El-Husseiny, A., Alarifi, S. A., Mahmoud, M., & Abdulraheem, A. (2021) · Journal of Petroleum Exploration and Production Technology · doi:10.1007/s13202-021-01302-2 · Cited by 232
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.15217 · Cited by 101
“Anomaly detection techniques [Omar et al., 2013] can also be useful for flagging abnormal inputs likely to trigger bad behavior.”
MethodHandling Insider Threat Through Supervised Machine Learning Techniques
Janjua, F., Masood, A., Abbas, H., & Rashid, I. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.10.012 · Cited by 48
Anomaly Detection in Microservice-Based Systems
Nobre, J., Pires, E. J. S., & Reis, A. (2023) · Applied Sciences · doi:10.3390/app13137891 · Cited by 34
Metadata
- Authors
- Omar, S., Ngadi, A., & H. Jebur, H.
- Venue
- International Journal of Computer Applications
- Publisher
- Foundation of Computer Science
- Published
- 2013-10-18
- Volume
- 79
- Issue
- 2
- Pages
- 33-41
- ISSN
- 0975-8887 (ISSN-L)
- OpenAlex
- W2107528096
- Cited by
- 344
- References
- 42
- FWCI
- 4.41
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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