OWASP IoT Top 10 based Attack Dataset for Machine Learning
1
Issued Date
2022-01-01
Resource Type
ISSN
17389445
Scopus ID
2-s2.0-85127500446
Journal Title
International Conference on Advanced Communication Technology, ICACT
Volume
2022-February
Start Page
317
End Page
322
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Conference on Advanced Communication Technology, ICACT Vol.2022-February (2022) , 317-322
Suggested Citation
Min N.M., Visoottiviseth V., Teerakanok S., Yamai N. OWASP IoT Top 10 based Attack Dataset for Machine Learning. International Conference on Advanced Communication Technology, ICACT Vol.2022-February (2022) , 317-322. 322. doi:10.23919/ICACT53585.2022.9728969 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84645
Title
OWASP IoT Top 10 based Attack Dataset for Machine Learning
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Internet of Things (IoT) systems are highly susceptible to cyberattacks by nature with minimal security protections. Providing a massive attack surface for attackers, they automatically become easy targets with potentially catastrophic impacts. Researchers are currently focusing on developing various anomaly detection systems for IoT networks to deal with this situation. However, these systems require a comprehensive labeled attack dataset to classify the malicious traffic correctly. This paper presents a systematic approach to design and develop an IoT testbed to generate such an attack dataset, namely the AIoT-Sol Dataset. Our proposed dataset contains the benign traffic as well as the often-overlooked attack techniques in the existing IoT datasets. It encompasses 17 attack types from several categories: network attacks, web attacks, and web IoT message protocol attacks. We selected these attacks by referencing the Open Web Application Security Project (OWASP) IoT Top Ten. Also, we provide a mapping of possible attacks for all ten security risks.