The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
A bidirectional LSTM deep learning approach for intrusion detection
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
A detailed analysis of the KDD CUP 99 data set
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Yin, C., Zhu, Y., Fei, J., & He, X. (2017) · IEEE Access · doi:10.1109/access.2017.2762418 · Cited by 2,100
A Deep Learning Approach for Network Intrusion Detection System
Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016) · Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) · doi:10.4108/eai.3-12-2015.2262516 · Cited by 1,274
A data mining framework for building intrusion detection models
Lee, W., Stolfo, S. J., & Mok, K. W. (2003) · Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344) · doi:10.1109/secpri.1999.766909 · Cited by 1,148
A survey of emerging threats in cybersecurity
Jang-Jaccard, J., & Nepal, S. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.02.005 · Cited by 892
Cited by
Design and Development of RNN Anomaly Detection Model for IoT Networks
Ullah, I., & Mahmoud, Q. H. (2022) · IEEE Access · doi:10.1109/access.2022.3176317 · Cited by 270
Metadata
- Authors
- Imrana, Y., Xiang, Y., Ali, L., & Abdul-Rauf, Z.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 185
- Pages
- 115524
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W3185895012
- Cited by
- 432
- References
- 46
- FWCI
- 28.99
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A bidirectional LSTM deep learning approach for intrusion detection. https://app.cochat.ai/papers/10.1016/j.eswa.2021.115524
- The paper
- Imrana, Y., Xiang, Y., Ali, L., & Abdul-Rauf, Z. (2021). A bidirectional LSTM deep learning approach for intrusion detection. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2021.115524
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
