Classification with LSTM Networks in User Behaviour Analytics with Unbalanced Environment
Abstract
Abstract The paper describes the proposed approach for classification in an unbalanced class environment and demonstrates it in the context of an anomaly detection system in user behaviour. The proposed approach is aimed on lessening the number of false-negative cases – cases, when an anomaly was recognized as normal event. The proposed approach is based on implementing LSTM neural network; it includes pre-processing flow data and splitting it into subsets by the user groups and training individual LSTM network for each group. The proposed approach was tested by experimenting with different neural network types and structures, as also testing in balanced and unbalanced class environments, including testing with and without LSTM network.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Keywords deep learning and LSTM have shown a marked increase in average publication year, highlighting their more recent adoption as core methodological tools.
“Notably, keywords like”
From Application of Machine Learning and Deep Learning Techniques for Enhanced Insider Threat Detection in Cybersecurity: Bibliometric Review (2025) · cites this paper as [51]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295 · see citing works
Learning from class-imbalanced data: Review of methods and applications
Guo, H.-X., Li, Y.-J., Shang, J., Gu, M.-Y., Huang, Y.-Y., & Gong, B. (2017) · Expert systems with applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,212 · see citing works
Cost-sensitive boosting for classification of imbalanced data
Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.04.009 · Cited by 1,429 · see citing works
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393 · see citing works
Markov Chains: From Theory to Implementation and Experimentation
Gagniuc, P. A. (2017) · doi:10.1002/9781119387596 · Cited by 415 · see citing works
An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets
Wah, Y. B., Rani, K. A., Rahman, H. A. A., Fong, S. J., Khairudin, Z., & Abdullah, N. N. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-981-4585-18-7_2 · Cited by 299 · see citing works
On strategies for imbalanced text classification using SVM: A comparative study
Sun, A., Lim, E., & Liu, Y. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.07.011 · Cited by 257 · see citing works
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
Liu, Y., & Chen, Y.-T. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883013 · Cited by 205 · see citing works
Huang, Y., Hung, C.-M., & Jiau, H. C. (2005) · Nonlinear Analysis Real World Applications · doi:10.1016/j.nonrwa.2005.04.006 · Cited by 162 · see citing works
Xu, L., Chow, M., & Taylor, L. S. (2007) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.888990 · Cited by 136 · see citing works
A particle swarm based hybrid system for imbalanced medical data sampling
Yang, P., Xu, L., Zhou, B. B., Zhang, Z., & Zomaya, A. Y. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-s3-s34 · Cited by 92 · see citing works
Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery
Taft, L., Evans, R. S., Shyu, C., Egger, M. J., Chawla, N. V., Mitchell, J. A., Thornton, S. N., Bray, B. E., & Varner, M. W. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.09.001 · Cited by 91 · see citing works
Mine Classification With Imbalanced Data
Williams, D. P., Myers, V., & Silvious, M. S. (2009) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2009.2021964 · Cited by 91 · see citing works
Lu, J. W. Z., & Wang, D. (2008) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2008.01.035 · Cited by 74 · see citing works
Forecasting of ozone episode days by cost-sensitive neural network methods
Tsai, C.-H., Chang, L., & Chiang, H.-C. (2009) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2008.12.007 · Cited by 52 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
McCarthy, A., Ghadafi, E., Andriotis, P., & Legg, P. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2010010 · Cited by 86 · see citing works
Ofori, H. K., Bell-Dzide, K., Brown-Acquaye, W. L., Lempogo, F., Frimpong, S. O., Agbehadji, I. E., & Millham, R. C. (2025) · Symmetry · doi:10.3390/sym17101704 · Cited by 7 · see citing works
Xian, W., & Liu, H. (2022) · Journal of Mathematics · doi:10.1155/2022/3899801 · Cited by 1 · see citing works
Li, X., Hu, L., Lu, P., Huang, T., Yang, W., Lu, Q., Liang, H., & Lu, L. J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3183469 · Cited by 1 · see citing works
Fraud Call Identification Based on Broad Learning System and Convolutional Neural Networks
Li, S., Xu, G., & Liu, Y. (2022) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc56324.2022.10065991 · Cited by 1 · see citing works
Research on Interactive Product Design Based on User Behavior Data in Virtual Reality Environment
Liu, Z. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-3137 · Cited by 1 · see citing works
Niktabe, S., Sharma, D. P., & Lashkari, A. H. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08494-6
- Authors
- Parshutin, S., Kirshners, A., Kornijenko, Y., Zabiniako, V., Gasparovica-Asite, M., & Rozkalns, A.
- Affiliations
- Riga Technical University (LV)
- Corresponding author
- Parshutin, S.
- Venue
- Automatic Control and Computer Sciences
- Publisher
- Allerton Press
- Published
- 2021-01
- Volume
- 55
- Issue
- 1
- Pages
- 85-91
- ISSN
- 0146-4116 (ISSN-L)
- 1558-108X
- OpenAlex
- W3138043834
- Cited by
- 7
- References
- 17
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 4
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Automatic Control and Computer Sciences (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- anomaly detection, LSTM neural network, false negative reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
