MQTTset, a New Dataset for Machine Learning Techniques on MQTT
Open accessAbstract
IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 security research community uses TON_IoT, Bot-IoT, and MQTTset for evaluating FL-based cyber security solutions in IoT networks.
“For evaluating FL-based cyber security solutions in IoT networks, the security research community uses the following three datasets: TON_IoT [149], Bot-IoT [154], and MQTTset [156].”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [156]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · 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
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 · see citing works
Denial of service in sensor networks
Wood, A. D., & Stankovic, J. A. (2002) · Computer · doi:10.1109/mc.2002.1039518 · Cited by 1,720 · see citing works
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018) · IEEE Pervasive Computing · doi:10.1109/mprv.2018.03367731 · Cited by 1,668 · see citing works
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 1,298 · see citing works
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100059 · Cited by 841 · see citing works
A Supervised Intrusion Detection System for Smart Home IoT Devices
Anthi, E., Williams, L., Slowinska, M., Theodorakopoulos, G., & Burnap, P. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2926365 · Cited by 599 · see citing works
Moustafa, N., Turnbull, B., & Choo, K. R. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2871719 · Cited by 597 · see citing works
Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M., & Jain, R. K. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2912022 · Cited by 526 · see citing works
Improving efficiency and accuracy in multilingual entity extraction
Daiber, J., Jakob, M., Hokamp, C., et al. (2013) · International Conference on Semantic Systems · doi:10.1145/2506182.2506198 · Cited by 523 · see citing works
Naive Bayes vs decision trees in intrusion detection systems
Amor, N. B., Salem Benferhat, A., & Elouedi, Z. (2004) · ACM Symposium on Applied Computing · doi:10.1145/967900.967989 · Cited by 491 · see citing works
Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices
Eskandari, M., Janjua, Z. H., Vecchio, M., & Antonelli, F. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2970501 · Cited by 437 · see citing works
Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification
Sarıtaş, M. M., & Yaşar, A. (2019) · International Journal of Intelligent Systems and Applications in Engineering · doi:10.18201/ijisae.2019252786 · Cited by 408 · see citing works
Karimipour, H., Dehghantanha, A., Parizi, R. M., Choo, K. R., & Leung, H. (2019) · IEEE Access · doi:10.1109/access.2019.2920326 · Cited by 357 · see citing works
Catal, C., & Di̇ri̇, B. (2008) · Information Sciences · doi:10.1016/j.ins.2008.12.001 · Cited by 346 · see citing works
Cyber intrusion detection by combined feature selection algorithm
Mohammadi, S., Mirvaziri, H., Ahsaee, M. G., & Karimipour, H. (2018) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2018.11.007 · Cited by 296 · see citing works
Farivar, F., Haghighi, M. S., Jolfaei, A., & Alazab, M. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2956474 · Cited by 295 · see citing works
A Novel Hierarchical Intrusion Detection System Based on Decision Tree and Rules-Based Models
Ahmim, A., Maglaras, L. A., Ferrag, M. A., Derdour, M., & Janicke, H. (2019) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss.2019.00059 · Cited by 288 · see citing works
Deep Learning-Based Intrusion Detection for IoT Networks
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., & Robles-Kelly, A. (2019) · 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC) · doi:10.1109/prdc47002.2019.00056 · Cited by 269 · see citing works
A Novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks
Khraisat, A., Gondal, I., Vamplew, P. W., Kamruzzaman, J., & Alazab, A. (2019) · Electronics · doi:10.3390/electronics8111210 · Cited by 260 · see citing works
AI-Based Two-Stage Intrusion Detection for Software Defined IoT Networks
Li, J., Zhao, Z., Li, R., & Zhang, H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2883344 · Cited by 245 · see citing works
Hajimirzaei, B., & Navimipour, N. J. (2018) · ICT Express · doi:10.1016/j.icte.2018.01.014 · Cited by 213 · see citing works
Cyber Intrusion Detection Using Machine Learning Classification Techniques
Alqahtani, H., Sarker, I. H., Kalim, A., Hossain, S. M. M., Ikhlaq, S., & Hossain, S. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6648-6_10 · Cited by 190 · see citing works
Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm
Tan, X., Su, S., Huang, Z., Guo, X., Zuo, Z., Sun, X., & Li, L. (2019) · Sensors · doi:10.3390/s19010203 · Cited by 189 · see citing works
MQTT based home automation system using ESP8266
Kodali, R. K., & Soratkal, S. (2016) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc.2016.7906845 · Cited by 188 · see citing works
Correlation analysis of MQTT loss and delay according to QoS level
Lee, S., Kim, H., Hong, D.-K., et al. (2013) · International Conference on Information Networking · doi:10.1109/icoin.2013.6496715 · Cited by 175 · see citing works
MedBIoT: Generation of an IoT Botnet Dataset in a Medium-sized IoT Network
Guerra-Manzanares, A., Medina-Galindo, J., Bahşi, H., & Nõmm, S. (2020) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0009187802070218 · Cited by 165 · see citing works
Multiclass Classification Procedure for Detecting Attacks on MQTT‐IoT Protocol
Aláiz‐Moretón, H., Aveleira‐Mata, J., Ondicol-Garcia, J., et al. (2019) · Complexity · doi:10.1155/2019/6516253 · Cited by 144 · see citing works
MSML: A Novel Multilevel Semi-Supervised Machine Learning Framework for Intrusion Detection System
Yao, H., Fu, D., Zhang, P., Li, M., & Liu, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2873125 · Cited by 126 · see citing works
An in-depth experimental study of anomaly detection using gradient boosted machine
Tama, B. A., & Rhee, K.-H. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3128-z · Cited by 125 · see citing works
SlowITe, a Novel Denial of Service Attack Affecting MQTT
Vaccari, I., Aiello, M., & Cambiaso, E. (2020) · Sensors · doi:10.3390/s20102932 · Cited by 103 · see citing works
Self-citationSlow DoS attacks: definition and categorisation
Cambiaso, E., Papaleo, G., Chiola, G., et al. (2013) · International Journal of Trust Management in Computing and Communications · doi:10.1504/ijtmcc.2013.056440 · Cited by 98 · see citing works
Self-citationSurvey on Intrusion Detection System using Machine Learning Techniques
KishorWagh, S., Pachghare, V., & Kolhe, S. R. (2013) · International Journal of Computer Applications · doi:10.5120/13608-1412 · Cited by 92 · see citing works
Investigating Brute Force Attack Patterns in IoT Network
Stiawan, D., Idris, M. Y., Malik, R. F., et al. (2019) · Journal of Electrical and Computer Engineering · doi:10.1155/2019/4568368 · Cited by 91 · see citing works
IoT home gateway for auto-configuration and management of MQTT devices
Kim, S., Choi, H.-S., & Rhee, W.-S. (2015) · IEEE Conference on Wireless Sensors (ICWiSe) · doi:10.1109/icwise.2015.7380346 · Cited by 81 · see citing works
IoT-Flock: An Open-source Framework for IoT Traffic Generation
Ghazanfar, S., Hussain, F. B., Rehman, A., et al. (2020) · 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) · doi:10.1109/icetst49965.2020.9080732 · Cited by 72 · see citing works
Taxonomy of Slow DoS Attacks to Web Applications
Cambiaso, E., Papaleo, G., & Aiello, M. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-34135-9_20 · Cited by 66 · see citing works
Self-citationARTEMIS: An Intrusion Detection System for MQTT Attacks in Internet of Things
Ciklabakkal, E., Donmez, A., Erdemir, M., et al. (2019) · Proceedings/Proceedings - Symposium on Reliable Distributed Systems · doi:10.1109/srds47363.2019.00053 · Cited by 62 · see citing works
Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99
Kushwaha, P., Buckchash, H., & Raman, B. (2017) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2017.8227975 · Cited by 55 · see citing works
Li, Y., Chi, Z., Liu, X., et al. (2018) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3210240.3210346 · Cited by 54 · see citing works
A novel perspective on multiclass classification: Regular simplex support vector machine
Tang, L., Tian, Y., & Pardalos, P. M. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.026 · Cited by 51 · see citing works
Ghanem, W. A. H. M., & Jantan, A. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04655-2 · Cited by 47 · see citing works
MQTTSA: A Tool for Automatically Assisting the Secure Deployments of MQTT Brokers
Palmieri, A., Prem, P., Ranise, S., et al. (2019) · IEEE World Congress on Services (SERVICES) · doi:10.1109/services.2019.00023 · Cited by 40 · see citing works
Design and implementation of mobile health monitoring system based on MQTT protocol
Ding, Y., Fan, B., Xiaoming, K., et al. (2016) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec.2016.7867503 · Cited by 33 · see citing works
MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices
Shalaginov, A., Semeniuta, O., & Alazab, M. (2019) · IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion) · doi:10.1145/3368235.3368876 · Cited by 29 · see citing works
Efficient algorithm for intrusion attack classification by analyzing KDD Cup 99
Chandolikar, N., & Nandavadekar, V. (2012) · International Conference on Wireless and Optical Communications Networks (WOCN) · doi:10.1109/wocn.2012.6335546 · Cited by 27 · see citing works
Remotely Exploiting AT Command Attacks on ZigBee Networks
Vaccari, I., Cambiaso, E., & Aiello, M. (2017) · Security and Communication Networks · doi:10.1155/2017/1723658 · Cited by 27 · see citing works
Self-citationDesigning and Modeling the Slow Next DoS Attack
Cambiaso, E., Papaleo, G., Chiola, G., et al. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-19713-5_22 · Cited by 26 · see citing works
Self-citationEvaluating Security of Low-Power Internet of Things Networks
Vaccari, I. (2019) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/080202 · Cited by 20 · see citing works
Self-citationA Real-World Password Cracking Demonstration Using Open Source Tools for Instructional Use
Kakarla, T., Mairaj, A., & Javaid, A. Y. (2018) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2018.8500257 · Cited by 17 · see citing works
Deep Neural Network for Detection of Cyber Attacks
Komar, M., Dorosh, V., Hladiy, G., et al. (2018) · IEEE International Conference on System Analysis & Intelligent Computing (SAIC) · doi:10.1109/saic.2018.8516753 · Cited by 15 · see citing works
An effective cyber attack detection system based on an improved OMPCA
Elkhadir, Z., Chougdali, K., & Benattou, M. (2017) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom.2017.8238162 · Cited by 9 · see citing works
Technique for IoT Cyberattacks Detection Based on DNS Traffic Analysis.
Bobrovnikova, K., Lysenko, S., Gaj, P., et al. (2020) · Cited by 9 · see citing works
Anomaly Detection in IoT: Methods, Techniques and Tools
Vigoya, L., López-Vizcaíno, M. F., Fernández, D., et al. (2019) · XoveTIC Conference (XoveTIC) · doi:10.3390/proceedings2019021004 · Cited by 7 · see citing works
Oluranti, J., Omoregbe, N. A., & Misra, S. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1299/1/012035 · Cited by 7 · see citing works
Mitigation Against Denial-of-Service Flooding and Malformed Packet Attacks
Patil, A. R. B., & Thakur, N. V. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1165-9_30 · Cited by 6 · see citing works
Solutions for deep packet inspection in industrial communications
Zamfir, S., Bălan, T., Sandu, F. D., et al. (2016) · International Conference on Communications (COMM) · doi:10.1109/iccomm.2016.7528337 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [1]Li Y. Chi Z. Liu X. Zhu T. Chiron: Concurrent high throughput communication for iot devices Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services Munich, Germany 11–15 June 2018 204 216
- [8]Soni D. Makwana A. A survey on mqtt: A protocol of internet of things (iot) Proceedings of the International Conference On Telecommunication, Power Analysis And Computing Techniques (ICTPACT-2017) Chennai, India 6–8 April 2017
Cited by
Sorted by citations
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works
A Framework for Malicious Traffic Detection in IoT Healthcare Environment
Hussain, F., Abbas, S. G., Shah, G. A., Pires, I. M., Fayyaz, U. U., Shahzad, F., Garcia, N. M., & Zdravevski, E. (2021) · Sensors · doi:10.3390/s21093025 · Cited by 230 · see citing works
A Systematic Review of Data-Driven Attack Detection Trends in IoT
Haque, S., El-Moussa, F., Komninos, N., & Muttukrishnan, R. (2023) · Sensors · doi:10.3390/s23167191 · Cited by 38 · see citing works
- Authors
- Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., & Cambiaso, E.
- Affiliations
- Institute of Electronics, Computer and Telecommunication Engineering (IT)
- University of Genoa (IT)
- Corresponding author
- Vaccari, Ivan
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-11-18
- Volume
- 20
- Issue
- 22
- Pages
- 6578
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7698741
- OpenAlex
- W3098620325
- Cited by
- 292
- References
- 60
- Citations by year
- 2026: 48 · 2025: 58 · 2024: 68 · 2023: 59 · 2022: 40 · 2021: 19
- FWCI
- 13.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- MQTT protocol, Internet of Things security, cyberattack detection, intrusion detection, IoT networks, network traffic data
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
