The DOI matches its Crossref record; 20 of the 27 references it cites were found in a registry or an index.
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
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“Many studies classify feature selection methods into three categories: filter-based techniques, wrapper techniques, and embed techniques”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [210]
Citations
Cites
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 · cited 3×
“KDD’99 (University of California, Irvine 1998-99): This dataset is an updated version of the DARPA98, by processing the tcpdump portion.”
BackgroundMethodSelf-citationTesting Intrusion detection systems
McHugh, J. (2000) · ACM Transactions on Privacy and Security · doi:10.1145/382912.382923 · Cited by 1,490 · cited 4×
“Moreover, it lacks actual attack data records (McHugh, 2000) (Brown et al.”
BackgroundMethodInfluentialToward developing a systematic approach to generate benchmark datasets for intrusion detection
Shiravi, A., Shiravi, H., Tavallaee, M., & Ghorbani, A. A. (2012) · Computers & Security · doi:10.1016/j.cose.2011.12.012 · Cited by 1,345
Self-citationCharacterization of Tor Traffic using Time based Features
Lashkari, A. H., Gil, G. D., Mamun, M., & Ghorbani, A. A. (2017) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0006105602530262 · Cited by 926 · cited 3×
“We begin to extract the 80 traffic features from the dataset using CICFlowMeter (CICFlowMeter, 2017),(Habibi Lashkari et al., 2017).”
MethodSelf-citationStatistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation
Song, J., Takakura, H., Okabe, Y., Eto, M., Inoue, D., & Nakao, K. (2011) · International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security · doi:10.1145/1978672.1978676 · Cited by 335 · cited 2×
“The normal traffic here has been simulated repeatedly during the attacks and producing only DNS and mail traffic data, which is not reflected in real world normal network traffic, so there are no false positives, which are important for minimizing the number of alerts (Song et al., 2011) (M.”
BackgroundGeneration of a new IDS test dataset: Time to retire the KDD collection
Creech, G., & Hu, J.-K. (2013) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2013.6555301 · Cited by 326 · cited 1×
“ADFA (University of New South Wales 2013): This dataset includes normal training and validating data and 10 attacks per vector (Creech and Hu, 2013).”
MethodTowards a Reliable Intrusion Detection Benchmark Dataset
Sharafaldin, I., Gharib, A., Lashkari, A. H., & Ghorbani, A. A. (2017) · Software Networking · doi:10.13052/jsn2445-9739.2017.009 · Cited by 309 · cited 9×
“Finally, we evaluate the quality of the generated dataset by searching for common mistakes and criticisms of other synthetically created datasets, based on the 11 criteria from the last proposed dataset evaluation framework by Canadian Institute for Cybersecurity (CIC)(Sharafaldin et al., 2017).”
MethodInfluentialSelf-citationAn Evaluation Framework for Intrusion Detection Dataset
Gharib, A., Sharafaldin, I., Lashkari, A. H., et al. (2016) · International Conferences on Information Science and System · doi:10.1109/icissec.2016.7885840 · Cited by 224 · cited 6×
“Regarding to the last dataset evaluation framework published on 2016 (Gharib et al., 2016), covering eleven criteria is necessary for each dataset.”
BackgroundMethodInfluentialSelf-citationNetwork Intrusion Detection and Prevention - Concepts and Techniques
Ghorbani, A., Lu, W., & Tavallaee, M. (2009) · Advances in Information Security · doi:10.1007/978-0-387-88771-5 · Cited by 175 · cited 1×
“Since 1999, Scott et al. (Scott and Wilkins, 1999), Heideman and Papadopulus (Heidemann and Papdopoulos, 2009), Ghorbani et al. (Ghorbani Ali and Mahbod, 2010), Nehinbe (Nehinbe, 2011), Shiravi et al. (Ali Shiravi and Ghorbani, 2012), and Sharfaldin et al. (Gharib et al., 2016) tried to propose an…”
BackgroundSelf-citationA Labeled Data Set for Flow-Based Intrusion Detection
Sperotto, A., Sadre, R., van Vliet, F., & Pras, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04968-2_4 · Cited by 155 · cited 1×
“It is labelled and is more realistic, but the lack of volume and diversity of attacks is obvious (Sperotto et al., 2009).”
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Cited by
Sorted by citations
Survey of intrusion detection systems: Techniques, datasets and challenges
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019) · Cybersecurity · doi:10.1186/s42400-019-0038-7 · Cited by 2,024
“This dataset contains network traffic traces from Distributed Denial-of-Service (DDoS) attacks, and was collected in 2007 (Hick et al., 2007).”
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.041 · Cited by 1,983
“Comparable to [25, 26] but more elaborate in their implementation, Sharafaldin et al. [8] crafted a testbed relying on physical machines, which were separated into two networks, the Victim-network and the Attack-network.”
BackgroundMethodInfluentialDeep Learning Approach for Intelligent Intrusion Detection System
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019) · IEEE Access · doi:10.1109/access.2019.2895334 · Cited by 2,228
“The details of various IDS datasets are discussed in [59] and [66].”
BackgroundAhmad, Z., Khan, A. S., Shiang, C. W., Abdullah, J., & Ahmad, F. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4150 · Cited by 1,514
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
Ferrag, M. A., Maglaras, L. A., Moschoyiannis, S., & Janicke, H. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102419 · Cited by 1,386
“[6], which implements attacks include Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS, and Brute Force FTP.”
Background
Metadata
- Authors
- Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A.
- Affiliations
- University of New Brunswick (CA)
- University of New Brunswick (UNB), Canada
- Venue
- Proceedings of the 4th International Conference on Information Systems Security and Privacy
- Published
- 2018
- Pages
- 108-116
- OpenAlex
- W2789828921
- Cited by
- 5,318
- References
- 27
- Citations by year
- 2026: 1170 · 2025: 1032 · 2024: 803 · 2023: 704 · 2022: 558 · 2021: 518 · 2020: 332 · 2019: 165 · 2018: 35
- FWCI
- 215.46
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Conference on Information Systems Security and Privacy (ICISSP) (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- intrusion detection, intrusion detection datasets, network traffic characterization, network security, dataset construction
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