Abstract
In connection with the classification of different Internet of things (IoT) assets, techniques are presented herein that enhance the classification process through a hybrid approach which utilizes both active monitoring techniques (where different packets are sent to a target and the response from same is compared with a database) and passive monitoring techniques (where a sniffing tool is used to non-intrusively identify a unique signature of a device). Aspects of the presented techniques employ active monitoring during a preliminary stage (to create an initial fingerprint of the network) and later employ passive monitoring (for gathering and calculating observed fingerprints). Such an approach yields an improved classification of IoT assets so that the proper segmentation of a network may be realized and even enhanced for anomaly-based intrusion detection. Additionally, the presented techniques are also less power-intensive, which is particularly important in an IoT environment.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chandran, Ajith, "HYBRID FINGERPRINT TECHNIQUE FOR IOT ASSET CLASSIFICATION AND MANAGEMENT", Technical Disclosure Commons, (April 12, 2022)
https://www.tdcommons.org/dpubs_series/5065