Abstract
We present P4Xtnd, which implements P4 programmability on resource-constrained devices with extended network functionalities for malicious device identification in IoT networks. Compared to existing methods, P4Xtnd operates in realtime and distributed manner for traffic collection, running ML at P4 Data plane with Federated Learning. Moreover, algorithms are proposed for trust assessment of sensor devices and dynamic network slicing for effective network management. With three LAN networks P4Xtnd shows its ability to detect malicious activities at higher accuracy.
Original language | English |
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Title of host publication | SIGCOMM Posters and Demos '24 |
Subtitle of host publication | proceedings of the 2024 SIGCOMM Poster and Demo Sessions |
Place of Publication | New York |
Publisher | Association for Computing Machinery |
Pages | 104-106 |
Number of pages | 3 |
ISBN (Electronic) | 9798400707179 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 SIGCOMM Poster and Demo Sessions, SIGCOMM'24 - Sydney, Australia Duration: 4 Aug 2024 → 8 Aug 2024 |
Conference
Conference | 2024 SIGCOMM Poster and Demo Sessions, SIGCOMM'24 |
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Country/Territory | Australia |
City | Sydney |
Period | 4/08/24 → 8/08/24 |
Keywords
- P4 programming
- In-network ML
- Software Defined Networking