23 citations · 74 across the 26 of their papers we have counts for
8 papers · 1 filter
Anomaly Detection in Power Markets and Systems
Ugur Halden, Umit Cali, Ferhat Ozgur Catak +2
The widespread use of information and communication technology (ICT) over the course of the last decades has been a primary catalyst behind the digitalization of power systems. Mea…
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks
M. Kuzlu, F. O. Catak, S. Sarp +2
With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG…
Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals
Ferhat Ozgur Catak, Murat Kuzlu, Salih Sarp +2
Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telecommunication…
Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data
Yuval Abraham Regev, Henrik Vassdal, Ugur Halden +2
Over the last few decades, extensive use of information and communication technologies has been the main driver of the digitalization of power systems. Proper and secure monitoring…
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 Catak +2
Future wireless networks (5G and beyond) are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks…
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case
Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp +2
Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the…
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining
Murat Kuzlu, Ferhat Ozgur Catak, Umit Cali +2
The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting t…
Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness
Omer Faruk Tuna, Ferhat Ozgur Catak, M. Taner Eskil
In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax functio…