A lightweight supervised intrusion detection mechanism for IoT networks
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The C4.5, ID3, and CART algorithms are among the well-known decision tree algorithms used for intrusion detection.
“The C4.5 algorithm [200], the ID3 algorithm [199], and the CART algorithm [55] are among the well-known decision tree algorithms used for intrusion detection.”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [200]
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
A Caution Regarding Rules of Thumb for Variance Inflation Factors
O’brien, R. M. (2007) · Quality & Quantity · doi:10.1007/s11135-006-9018-6 · Cited by 11,217 · see citing works
Abdi, H., & Williams, L. J. (2010) · WIREs Computational Statistics · doi:10.1002/wics.101 · Cited by 11,104 · see citing works
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 · see citing works
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033 · see citing works
Deep 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 2,126 · see citing works
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 · see citing works
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies
P. Vatcheva, K., & Lee, M. (2016) · Epidemiology: Open Access · doi:10.4172/2161-1165.1000227 · Cited by 1,291 · see citing works
SMOTE for high-dimensional class-imbalanced data
Blagus, R., & Lusa, L. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-106 · Cited by 1,155 · see citing works
Distributed attack detection scheme using deep learning approach for Internet of Things
Diro, A. A., & Chilamkurti, N. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.043 · Cited by 1,100 · see citing works
Pattern recognition and machine learning
(1993) · Choice Reviews Online · doi:10.5860/choice.30-3308 · Cited by 1,046 · see citing works
A survey of intrusion detection in Internet of Things
Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.02.009 · Cited by 1,022 · see citing works
Random Forest Modeling for Network Intrusion Detection System
Farnaaz, N., & Jabbar, M. A. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.06.047 · Cited by 702 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Wiley Interdisciplinary Reviews: Computational Statistics
Wiley, J., & Abdi, H. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/(issn)1939-0068 · Cited by 483 · see citing works
WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks
Almomani, I., Kasasbeh, B., & Al-Akhras, M. (2016) · Journal of Sensors · doi:10.1155/2016/4731953 · Cited by 420 · see citing works
Automatic dimensionality selection from the scree plot via the use of profile likelihood
Zhu, M., & Ghodsi, A. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2005.09.010 · Cited by 383 · see citing works
Statistical 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 · see citing works
A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network
Li, W., Yi, P., Wu, Y., Pan, L., & Li, J. (2014) · Journal of Electrical and Computer Engineering · doi:10.1155/2014/240217 · Cited by 297 · see citing works
Self-citationCombating imbalance in network intrusion datasets
Cieslak, D. A., Chawla, N. V., & Striegel, A. (2006) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2006.1635905 · Cited by 286 · see citing works
Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset
Yulianto, A., Sukarno, P., & Suwastika, N. A. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1192/1/012018 · Cited by 271 · see citing works
Intrusion detection using deep belief networks
Alom, M. Z., Bontupalli, V., & Taha, T. M. (2015) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2015.7443094 · Cited by 265 · see citing works
Partial Least Squares Regression
Helland, I. S. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess6004.pub2 · Cited by 263 · see citing works
GAN-based imbalanced data intrusion detection system
Lee, J., & Park, K. (2019) · Personal and Ubiquitous Computing · doi:10.1007/s00779-019-01332-y · Cited by 248 · see citing works
Hybrid approach to intrusion detection in fog-based IoT environments
de Souza, C. A., Westphall, C. B., Machado, R. B., Sobral, J. B. M., & Vieira, G. D. S. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107417 · Cited by 163 · see citing works
HML-IDS: A Hybrid-Multilevel Anomaly Prediction Approach for Intrusion Detection in SCADA Systems
Khan, I. A., Pi, D., Khan, Z. U., Hussain, Y., & Nawaz, A. (2019) · IEEE Access · doi:10.1109/access.2019.2925838 · Cited by 156 · see citing works
Decision Tree Based Intrusion Detection System for NSL-KDD Dataset
Ingre, B., Yadav, A., & Soni, A. K. (2018) · Smart Innovation, Systems and Technologies · doi:10.1007/978-3-319-63645-0_23 · Cited by 150 · see citing works
Elhassan, A. T., Aljourf, M., Al-Mohanna, F. A., & Shoukri, M. (2016) · Global Journal of Technology and Optimization · doi:10.4172/2229-8711.s1111 · Cited by 139 · see citing works
A fast string-matching algorithm for network processor-based intrusion detection system
Liu, R.-T., Huang, N.-F., Chen, C., et al. (2004) · ACM Transactions on Embedded Computing Systems · doi:10.1145/1015047.1015055 · Cited by 98 · see citing works
Attention based multi-agent intrusion detection systems using reinforcement learning
Sethi, K., Madhav, Y. V., Kumar, R., & Bera, P. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102923 · Cited by 91 · see citing works
Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN
Shapoorifard, H., & Shamsinejad, P. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017914340 · Cited by 77 · see citing works
How ordinary elimination became Gaussian elimination
Grcar, J. F. (2010) · Historia Mathematica · doi:10.1016/j.hm.2010.06.003 · Cited by 66 · see citing works
Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection
Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K. K., Morabito, F. C., Larijani, H., Raza, A., & Hussain, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00563-4_74 · Cited by 66 · see citing works
Convolutional Neural Networks with LSTM for Intrusion Detection
Ahsan, M., & Nygard, K. E. (2020) · EPiC series in computing · doi:10.29007/j35r · Cited by 57 · see citing works
Boukhamla, A. Z. E., & Gaviro, J. C. (2021) · International Journal of Information and Computer Security · doi:10.1504/ijics.2021.117392 · Cited by 45 · see citing works
A Novel Random Neural Network Based Approach for Intrusion Detection Systems
Qureshi, A.-U.-H., Larijani, H., Ahmad, J. E., et al. (2018) · Computer Science and Electronic Engineering Conference · doi:10.1109/ceec.2018.8674228 · Cited by 33 · see citing works
Partial Least‐Squares Regression
Andrade, J. M., Carlosena, A., Boqué, R., & Ferré, J. (2013) · Royal Society of Chemistry eBooks · doi:10.1039/9781849739344-00280 · Cited by 21 · see citing works
A Lightweight Signature-Based IDS for IoT EnvironmentPreprint
Sheikh, N. U., Rahman, H., Vikram, S., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.04582 · Cited by 20 · see citing works
Intrusion detection in Cloud Internet of Things environment
Rebbah, M., Rebbah, D. E., & Smail, O. (2017) · International Conference on Mathematics and Information Technology (ICMIT) · doi:10.1109/mathit.2017.8259697 · Cited by 12 · see citing works
Song, C., Fan, W., Chang, S., et al. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-72725-3_13 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works
A Lightweight IoT Intrusion Detection Model Based on Improved BERT-of-Theseus
Wang, Z., Li, J., Yang, S., Luo, X., Li, D., & Mahmoodi, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122045 · Cited by 131 · see citing works
Self-citationA Novel Lightweight IoT Intrusion Detection Model Based on Self-Knowledge Distillation
Wang, Z., Zhou, R., Yang, S., He, D., & Chan, S. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3533092 · Cited by 38 · see citing works
- Authors
- Roy, S., Li, J., Choi, B.-J., & Bai, Y.
- Affiliations
- North Dakota State University (US)
- University of Washington Tacoma (US)
- Corresponding author
- Li, Juan
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 127
- Pages
- 276-285
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3202083238
- Cited by
- 192
- References
- 40
- Citations by year
- 2026: 24 · 2025: 51 · 2024: 58 · 2023: 38 · 2022: 19 · 2021: 2
- FWCI
- 13.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 240 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Future Generation Computer Systems (acceptedVersion)
- Funders
- National Science Foundation (1722913, 1921576)
- NSF
- 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 › Information Systems › Internet of Things and AI
- Keywords
- intrusion detection, Internet of Things security, lightweight intrusion detection, resource-constrained devices, cyberattack detection, anomaly detection, sampling, dimensionality reduction, feature selection, CICIDS2017, NSL-KDD dataset, false alarm rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review