23 citations · 74 across the 26 of their papers we have counts for
27 papers
Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks
Ferhat Ozgur Catak, Umit Cali, Murat Kuzlu +1
With the rise of intelligent applications, such as self-driving cars and augmented reality, the security and reliability of wireless communication systems have become increasingly…
5G-SRNG: 5G Spectrogram-based Random Number Generation for Devices with Low Entropy Sources
Ferhat Ozgur Catak, Evren Catak, Ogerta Elezaj
Random number generation (RNG) is a crucial element in security protocols, and its performance and reliability are critical for the safety and integrity of digital systems. This is…
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…
Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems
Kevser Şahinbaş, Ferhat Ozgur Catak
Recently, many innovations have been experienced in healthcare by rapidly growing Internet-of-Things (IoT) technology that provides significant developments and facilities in the h…
Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving
Ferhat Ozgur Catak, Tao Yue, Shaukat Ali
Object detection in autonomous cars is commonly based on camera images and Lidar inputs, which are often used to train prediction models such as deep artificial neural networks for…
Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction
Ferhat Ozgur Catak, Evren Catak, Murat Kuzlu +2
6G -- sixth generation -- is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning algorithms have been a…
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…
Internet of Predictable Things (IoPT) Framework to Increase Cyber-Physical System Resiliency
Umit Cali, Murat Kuzlu, Vinayak Sharma +2
During the last two decades, distributed energy systems, especially renewable energy sources (RES), have become more economically viable with increasing market share and penetratio…
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…
A Generative Model based Adversarial Security of Deep Learning and Linear Classifier Models
erhat Ozgur Catak, Samed Sivaslioglu, Kevser Sahinbas
In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. With the rapid developments of deep…
Data Augmentation Based Malware Detection using Convolutional Neural Networks
Ferhat Ozgur Catak, Javed Ahmed, Kevser Sahinbas +1
Recently, cyber-attacks have been extensively seen due to the everlasting increase of malware in the cyber world. These attacks cause irreversible damage not only to end-users but…
A Benchmark API Call Dataset for Windows PE Malware Classification
Ferhat Ozgur Catak, Ahmet Faruk Yazı
The use of operating system API calls is a promising task in the detection of PE-type malware in the Windows operating system. This task is officially defined as running malware in…
Privacy Preserving PageRank Algorithm By Using Secure Multi-Party Computation
Ferhat Ozgur Catak
In this work, we study the problem of privacy preserving computation on PageRank algorithm. The idea is to enforce the secure multi party computation of the algorithm iteratively u…
Secure Multi-Party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data
Ferhat Özgür Çatak
Especially in the Big Data era, the usage of different classification methods is increasing day by day. The success of these classification methods depends on the effectiveness of…
Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVM
Ferhat Özgür Çatak
In machine learning area, as the number of labeled input samples becomes very large, it is very difficult to build a classification model because of input data set is not fit in a…
Classification with Boosting of Extreme Learning Machine Over Arbitrarily Partitioned Data
Ferhat Özgür Çatak
Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting useful predictive modeling from these…