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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From Edge Transformer to IoT Decisions: Offloaded Embeddings for Lightweight Intrusion Detection
Adjewa, F., Esseghir, M., & Merghem-Boulahia, L. (2026) · Sensors · doi:10.3390/s26020356 · Cited by 23
One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study
Paolini, D., Dini, P., Soldaini, E., & Saponara, S. (2025) · Computers · doi:10.3390/computers14070281 · Cited by 21
Xiao, F., Chen, S., Yang, J., He, H., Jiang, X., Tan, X., & Jin, D. (2025) · Computers & Security · doi:10.1016/j.cose.2024.104180 · Cited by 17
An Optimized Hybrid Deep Learning Framework for Monitoring Botnet Attacks in IoT Networks
A, M., J, S. K. P., L, K., D, K., & R, S. (2024) · 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis64247.2024.10823344 · Cited by 16
Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15
Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis
Szczepanik, W., & Niemiec, M. (2022) · Energies · doi:10.3390/en15113951 · Cited by 9
Bringing Machine Learning Classifiers Into Critical Cyber-Physical Systems: A Matter of Design
Sayin, B., Zoppi, T., Marchini, N., Khokhar, F. A., & Passerini, A. (2025) · IEEE Access · doi:10.1109/access.2025.3568501 · Cited by 7
“ADFANet [57], AndMal17 [58], CICIDS17 [59], CICIDS18 [59], CIDDS [60], IoT Network [61], ISCX12 [62], NSLKDD [63], SDN20 [64], UGR16 [65], and UNSW-NB15 [66] datasets contain data during normal network operation and during cyber-attacks.”
Explainable AI: enhancing decision-making in the detection of cyber threats
Prasad, P. W. C., Sayeed, M. S., Nguyen, D.-M., Hutabarat, D. P., & Mohiuddin, G. M. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1762332 · Cited by 16
A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
Jiang, L., Ryan, R., Li, Q., & Ferdosian, N. (2025) · arXiv · doi:10.48550/arxiv.2510.26307 · Cited by 13
Mohamed, A. S., Fritz, C., Saber, A. M., Ma, Y., Kaur, R., Al-Darwbi, M., Friedrich, D., & Kundur, D. (2026) · arXiv · doi:10.48550/arxiv.2609.00171
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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