The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
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Citations
Cites
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
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
Enhanced Network Anomaly Detection Based on Deep Neural Networks
Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K. (2018) · IEEE Access · doi:10.1109/access.2018.2863036 · Cited by 507
Secure audit logs to support computer forensics
Schneier, B., & Kelsey, J. (1999) · ACM Transactions on Information and System Security · doi:10.1145/317087.317089 · Cited by 443
An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units
Xu, C., Shen, J., Du, X., & Zhang, F. (2018) · IEEE Access · doi:10.1109/access.2018.2867564 · Cited by 339
Multilevel Modeling of Categorical Outcomes Using IBM SPSS
Heck, R. H., Thomas, S., & Tabata, L. (2013) · doi:10.4324/9780203808986 · Cited by 316
Cited by
A Novel Deep Learning-Based Intrusion Detection System for IoT Networks
Awajan, A. (2023) · Computers · doi:10.3390/computers12020034 · Cited by 292
Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265
GNN-IDS: Graph Neural Network based Intrusion Detection System
Sun, Z., Teixeira, A. M. H., & Toor, S. (2024) · Proceedings of the 19th International Conference on Availability, Reliability and Security · doi:10.1145/3664476.3664515 · Cited by 80
Metadata
- Authors
- Thakkar, A., & Lohiya, R.
- Venue
- Artificial Intelligence Review
- Published
- 2021-07-01
- Volume
- 55
- Issue
- 1
- Pages
- 453-563
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3186172578
- Cited by
- 350
- References
- 10
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. https://app.cochat.ai/papers/10.1007/s10462-021-10037-9
- The paper
- Thakkar, A., & Lohiya, R. (2022). A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intelligence Review. https://doi.org/10.1007/s10462-021-10037-9
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.
