The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
Open accessAbstract
Intrusion detection systems (IDSs) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles are proposed. A hybrid feature selection technique comprising three methods, i.e., particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensemble based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. The results regarding the UNSW-NB15 dataset also improve the ones achieved by several state-of-the-art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by the IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier.
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Building an efficient intrusion detection system based on feature selection and ensemble classifier
Zhou, Y., Cheng, G., Jiang, S., & Dai, M. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107247 · Cited by 675
A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer
Alazzam, H., Sharieh, A., & Sabri, K. E. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113249 · Cited by 398
Intrusion detection systems using long short-term memory (LSTM)
Laghrissi, F., Douzi, S., Douzi, K., & Hssina, B. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00448-4 · Cited by 304
A systematic literature review for network intrusion detection system (IDS)
Abdulganiyu, O. H., Ait Tchakoucht, T., & Saheed, Y. K. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00682-2 · Cited by 279
Towards the design of real-time autonomous IoT NIDS
Alhowaide, A., Alsmadi, I., & Tang, J. (2023) · Cluster Computing · doi:10.1007/s10586-021-03231-5 · Cited by 43
Metadata
- Authors
- Tama, B. A., Comuzzi, M., & Rhee, K.-H.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 94497-94507
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2958489519
- Cited by
- 386
- References
- 58
- FWCI
- 23.82
- Type
- journal article
- Language
- en
- Open access
- gold
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- Tama, B. A., Comuzzi, M., & Rhee, K.-H. (2019). TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System. IEEE Access. https://doi.org/10.1109/access.2019.2928048
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