Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless — NSF Award to Johns Hopkins University
The transition to 5G is expected to witness not only an emergence of new applications such as mobile augmented and virtual reality, but also opens up the attack surface to both known, and previously unknown threats. Thus, wireless networks of the future will need better control and management at different temporal and
| Award title | Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless |
|---|---|
| Award ID | 2528780 |
| Awardee | Johns Hopkins University |
| City | BALTIMORE |
| State | MD |
| Amount obligated | $165,874 |
| Principal investigator | Vladimir Braverman |
| Program | Networking Technology and Syst |
| Start date | 01/01/2025 |
| Abstract | The transition to 5G is expected to witness not only an emergence of new applications such as mobile augmented and virtual reality, but also opens up the attack surface to both known, and previously unknown threats. Thus, wireless networks of the future will need better control and management at different temporal and traffic aggregation granularities (e.g., how to allocate spectrum, how to quarantine distributed attacks etc.). This project aims to develop scalable, machine learning based analyt |
| Source | NSF Awards |
$799/mo
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