The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A survey of network-based intrusion detection data sets
Open accessAbstract
Abstract Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work provides a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet- and flow-based network data in detail. The paper identifies 15 different properties to assess the suitability of individual data sets for specific evaluation scenarios. These properties cover a wide range of criteria and are grouped into five categories such as data volume or recording environment for offering a structured search. Based on these properties, a comprehensive overview of existing data sets is given. This overview also highlights the peculiarities of each data set. Furthermore, this work briefly touches upon other sources for network-based data such as traffic generators and data repositories. Finally, we discuss our observations and provide some recommendations for the use and the creation of network-based data sets.
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
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
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
An empirical comparison of botnet detection methods
García, S., Grill, M., Stiborek, J., & Zunino, A. (2014) · Computers & Security · doi:10.1016/j.cose.2014.05.011 · Cited by 962
Fontugne, R., Borgnat, P., Abry, P., & Fukuda, K. (2010) · Proceedings of the 6th International COnference · doi:10.1145/1921168.1921179 · Cited by 295
Cited by
Moustafa, N. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102994 · Cited by 721
Deep learning methods in network intrusion detection: A survey and an objective comparison
Gamage, S., & Samarabandu, J. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102767 · Cited by 382
Application of deep reinforcement learning to intrusion detection for supervised problems
Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112963 · Cited by 360
The Role of Machine Learning in Cybersecurity
Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Brdalo Rapa, L., Grammatopoulos, A. V., & Di Franco, F. (2023) · Digital Threats: Research and Practice · doi:10.1145/3545574 · Cited by 279
LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection
Damasevicius, R., Venckauskas, A., Grigaliunas, S., Toldinas, J., Morkevicius, N., Aleliunas, T., & Smuikys, P. (2020) · Electronics · doi:10.3390/electronics9050800 · Cited by 132
Metadata
- Authors
- Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A.
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 86
- Pages
- 147-167
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W2924689635
- Cited by
- 947
- References
- 5
- FWCI
- 49.85
- Type
- journal article
- Language
- en
- Open access
- green
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