5 citations · 18 across the 18 of their papers we have counts for
18 papers
Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks
Ferhat Ozgur Catak, Murat Kuzlu, Jungwon Seo +1
This paper presents a federated learning framework secured by quantum key distribution (QKD) for wireless channel estimation and radar spectrum sensing in the next generation netwo…
Cyber-Resilient Data-Driven Event-Triggered Secure Control for Autonomous Vehicles Under False Data Injection Attacks
Yashar Mousavi, Mahsa Tavasoli, Ibrahim Beklan Kucukdemiral +4
This paper proposes a cyber-resilient secure control framework for autonomous vehicles (AVs) subject to false data injection (FDI) threats as actuator attacks. The framework integr…
Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era
Ferhat Ozgur Catak, Jungwon Seo, Umit Cali
Quantum machine learning (QML) is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanic…
Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative a…
BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
Massive MIMO (Multiple-Input Multiple-Output) is an advanced wireless communication technology, using a large number of antennas to improve the overall performance of the communica…
Neural Networks Meet Elliptic Curve Cryptography: A Novel Approach to Secure Communication
Mina Cecilie Wøien, Ferhat Ozgur Catak, Murat Kuzlu +1
In recent years, neural networks have been used to implement symmetric cryptographic functions for secure communications. Extending this domain, the proposed approach explores the…
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…
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…
LCOE-based Pricing for DLT-enabled Local Energy Trading Platforms
Marthe Fogstad Dynge, Ugur Halden, Gro Klæboe +1
Support schemes like the Feed-in-Tariff (FiT) have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism t…
Cybersecurity of Renewable Energy Data and Applications Using Distributed Ledger Technology
Umit Cali, Murat Kuzlu, Manisa Pipattanasomporn +2
Renewable energy sources (RES) are among the most popular emerging energy resources during the past two decades. Many countries have introduced various energy policy instruments, s…
Analysis of False Data Injection Impact on AI based Solar Photovoltaic Power Generation Forecasting
S. Sarp, M. Kuzlu, U. Cali +2
The use of solar photovoltaics (PV) energy provides additional resources to the electric power grid. The downside of this integration is that the solar power supply is unreliable a…
Development of A Load Control Algorithm to Enhance Energy Sustainability for the International Space Station
E. Herwald, G. Holliday, M. Kuzlu +2
This paper presents a load control algorithm for control of energy sources and loads to enhance energy sustainability and reliability of the International Space Station (ISS), whic…