Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case

12 March 2021

Papers citing "Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case"

8 / 8 papers shown

Title
Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services Denis Parfenov Anton Parfenov AAML 22 0 0 01 May 2025
Towards Secured Smart Grid 2.0: Exploring Security Threats, Protection Models, and Challenges Lan-Huong Nguyen V. Nguyen Ren-Hung Hwang Jian-Jhih Kuo Yu-Wen Chen Chien-Chung Huang Ping-I Pan 34 6 0 07 Nov 2024
5G-SRNG: 5G Spectrogram-based Random Number Generation for Devices with Low Entropy Sources Ferhat Ozgur Catak Evren Çatak Ogerta Elezaj 11 0 0 19 Apr 2023
Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals Ferhat Ozgur Catak Murat Kuzlu S. Sarp Evren Çatak Umit Cali AAML 20 3 0 27 Sep 2022
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks Ferhat Ozgur Catak Murat Kuzlu Evren Çatak Umit Cali Ozgur Guler AAML 17 26 0 12 Aug 2022
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining Murat Kuzlu Ferhat Ozgur Catak Umit Cali Evren Çatak Ozgur Guler AAML 24 9 0 16 Feb 2022
Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction Ferhat Ozgur Catak Evren Çatak Murat Kuzlu Umit Cali Devrim Unal AAML 35 44 0 09 May 2021
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016