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TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
Open accessAbstract
Although the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks. The potential threats to IoT applications and the need to reduce risk have recently become an interesting research topic. It is crucial that effective Intrusion Detection Systems (IDSs) tailored to IoT applications be developed. Such IDSs require an updated and representative IoT dataset for training and evaluation. However, there is a lack of benchmark IoT and IIoT datasets for assessing IDSs-enabled IoT systems. This paper addresses this issue and proposes a new data-driven IoT/IIoT dataset with the ground truth that incorporates a label feature indicating normal and attack classes, as well as a type feature indicating the sub-classes of attacks targeting IoT/IIoT applications for multi-classification problems. The proposed dataset, which is named TON_IoT, includes Telemetry data of IoT/IIoT services, as well as Operating Systems logs and Network traffic of IoT network, collected from a realistic representation of a medium-scale network at the Cyber Range and IoT Labs at the UNSW Canberra (Australia). This paper also describes the proposed dataset of the Telemetry data of IoT/IIoT services and their characteristics. TON_IoT has various advantages that are currently lacking in the state-of-the-art datasets: i) it has various normal and attack events for different IoT/IIoT services, and ii) it includes heterogeneous data sources. We evaluated the performance of several popular Machine Learning (ML) methods and a Deep Learning model in both binary and multi-class classification problems for intrusion detection purposes using the proposed Telemetry dataset.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
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
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 · cited 6×
“Various network datasets, for example, KDDCUP99, NSL-KDD [12], UNSW-NB15 [13] and ISCX [14], were generated for evaluating IDSs; however, they do not include any specific characteristics of IoT/IIoT applications as these datasets contain neither sensors’ reading data (i.e., telemetry or measurement data) nor IoT networ…”
BackgroundMethodInfluentialInternet of Things in industries: A survey
Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033
Self-citation
Research works printed but not linked: 43
- 2020, Grid Search
- 2020, Amazon Web Services (AWS) Lambda
- 2020, Microsoft Azure IoT Hub
- goodfellow, 2016, Deep Learning
- 2020, VMware VSphere Hypervisor
- 2020, The VMware NSX vCloud NFV platform
- 2020, Cloud NFV
- 2020, Ever Thought Virtualizing Your Network
- 2020, Vulnerable Public PHP Website
- 2020, HiveMQ
- 2019, Node-RED
- 2020, KALI Linux
- nelso, 2011, Common Cybersecurity Vulnerabilities in Industrial Control Systems, Control Syst Secur Program Washington DC Dept Homeland Secur (DHS) Nat Cyber Secur Division
- 2020, Cross-Site-Script Toolkit
- 2020, CeWL Package
- 2020, Hydra Package
- 2020, Ettercap
- han, 2011, Data Mining Concepts Techninqs
- 2019, KDD Cup 1999 data
- 10.1016/j.eswa.2010.04.017 · doi:10.1016/j.eswa.2010.04.017
- 10.1109/TAC.2019.2904438 · doi:10.1109/tac.2019.2904438
- moustafa, 2020, TON-IoT Dataset
- alsaedi, 2020, T-IIoT Dataset Evaluation
- 2020, ToN_IoT Attacks Scenarios
- 2020, UFONet Toolkit
- 2020, DoS & DDoS Attack using Scapy
- 2020, Nessus A Securce Vulnerability Scanning Tool
- 10.1145/2976749.2978293 · doi:10.1145/2976749.2978293
- 2020, PANDA
- 2020, SciPy
- 2020, TensorFlow™
- 2020, Scikit-Learn
- 2020, Numpy
- 2020, Keras
- 2020, ThingPulse ESP8266 Weather Station
- geller, 2020, Normalization vs Standardization Quantitative Analysis
- guyon, 1997, A Scaling Law for the Validation-set Training-set Size Ratio, AT&T-Bell Lab Int, 15, 1
- 2020, OWASP Security
- 2020, Metasploitable3
- 2019, Node-RED-Contrib-Modbus
- 2020, Security Onion
- 2020, VMware NSX
- 2020, DVWA
Cited by
Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625
“The authors used TON_IoT [186] and LITNET-2020 [187] to validate the robustness and efficiency of the framework over different cyberattack detection approaches.”
MethodGhimire, B., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150363 · Cited by 478
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450
Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead
Yue, P., An, J., Zhang, J., Ye, J., Pan, G., Wang, S., Xiao, P., & Hanzo, L. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3296160 · Cited by 291
Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems
Abdel-Basset, M., Moustafa, N., Hawash, H., Razzak, I., Sallam, K. M., & Elkomy, O. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3119968 · Cited by 161
Self-citation
Metadata
- Authors
- Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 165130-165150
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3085955590
- Cited by
- 892
- References
- 45
- FWCI
- 34.42
- Type
- journal article
- Language
- en
- Open access
- gold
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