Automatic network recognition offers a promising framework for the integration of the cognitive concept at the network layer. This work addresses the problem of automatic classification of technologies operating in the ISM band, with particular focus on Wi-Fi vs. Bluetooth recognition. The proposed classifier is based on feature extraction related to time-varying patterns of packet sequences, i.e. MAC layer procedures, and adopts different linear classification algorithms. Results of classification confirmed the ability to reveal both technologies based on Mac layer feature identification.
Automatic network recognition by feature extraction: A case study in the ISM band / DI BENEDETTO, Maria Gabriella; Stefano, Boldrini; C. j., Martin Martin; J., Roldan Diaz. - (2010). (Intervento presentato al convegno 2010 5th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCom 2010 tenutosi a Cannes; France nel 9 June 2010 through 11 June 2010) [10.4108/icst.crowncom2010.9274].
Automatic network recognition by feature extraction: A case study in the ISM band
DI BENEDETTO, Maria Gabriella;
2010
Abstract
Automatic network recognition offers a promising framework for the integration of the cognitive concept at the network layer. This work addresses the problem of automatic classification of technologies operating in the ISM band, with particular focus on Wi-Fi vs. Bluetooth recognition. The proposed classifier is based on feature extraction related to time-varying patterns of packet sequences, i.e. MAC layer procedures, and adopts different linear classification algorithms. Results of classification confirmed the ability to reveal both technologies based on Mac layer feature identification.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
