Consequently, conducting thorough and high-quality research in intrusion detection within the industrial IoT domain has become imperative. Machine learning is well suited to be used for intrusion detection in IoT scenarios in the face of increasing data volumes. We leveraged the ToN-IoT dataset, ...
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 ...
Multiplatform support, including Android, IoT Devices. Support offline model weight preprocessing while compiling. Support offline memory reuse computing for minimum runtime buffer size. Support kernel register for custom op. Third-party hardware like NNIE can be accessed through it. API Change API Inc...
The TON_IoT network dataset is validated using four machine learning-based intrusion detection algorithms of Gradient Boosting Machine, Random Forest, Naive Bayes, and Deep Neural Networks, revealing a high performance of detection accuracy using the set of training and testing. A comparative summary ...
In this paper, we introduce the description, statistical analysis, and machine learning evaluation of the novel ToN_IoT dataset. Comparison to other recent IoT datasets shows the importance of heterogeneity within these datasets, and how differences between datasets may have a huge impact on ...