5 citations · 16 across the 18 of their papers we have counts for
10 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…
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…
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…
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case
Evren Catak, Ferhat Ozgur Catak, Arild Moldsvor
6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and…
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples
Omer Faruk Tuna, Ferhat Ozgur Catak, M. Taner Eskil
Deep neural network architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully cra…
Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine Learning
Omer Faruk Tuna, Ferhat Ozgur Catak, M. Taner Eskil
While state-of-the-art Deep Neural Network (DNN) models are considered to be robust to random perturbations, it was shown that these architectures are highly vulnerable to delibera…