A Machine Learning-Driven Analysis of Phantom E911 Calls-Reference-Cited by-同舟云学术

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A Machine Learning-Driven Analysis of Phantom E911 Calls

Published:2022-05-16 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning
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Author:

Hu Yang¹,Taki Batoul²,Bajwa Waheed U.¹,Talasila Manoop³,Mantan Mukesh³,Aftab Syed Anwar³

Affiliation:

1. Rutgers University, Piscatway, NJ, USA

2. Rutgers Univeristy, Piscatway, NJ, USA

3. AT&T Labs, Bedminster, NJ, USA

Funder

National Science Foundation

Army Research Office

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3522783.3529527

Reference20 articles.

1. Effects of Signaling Attacks on LTE Networks

2. Security analysis of a femtocell device

3. Jin Cao , Maode Ma , Hui Li , Yueyu Zhang , and Zhenxing Luo . 2013. A survey on security aspects for LTE and LTE-A networks . IEEE communications surveys & tutorials, Vol. 16 , 1 ( 2013 ), 283--302. Jin Cao, Maode Ma, Hui Li, Yueyu Zhang, and Zhenxing Luo. 2013. A survey on security aspects for LTE and LTE-A networks. IEEE communications surveys & tutorials, Vol. 16, 1 (2013), 283--302.

4. Anomaly Detection Algorithms for the Sleeping Cell Detection in LTE Networks

5. Nico Golde , Kevin Redon , and Ravishankar Borgaonkar . 2012 . Weaponizing Femtocells: The Effect of Rogue Devices on Mobile Telecommunications.. In NDSS. Nico Golde, Kevin Redon, and Ravishankar Borgaonkar. 2012. Weaponizing Femtocells: The Effect of Rogue Devices on Mobile Telecommunications.. In NDSS.

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