Blar i UiS Brage på forfatter "Catak, Ferhat Özgur"
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5G RF Spectrum-based Cryptographic Pseudo Random Number Generation for IoT Security
Øksendal, Oddvar Nordbø (Master thesis, 2023)This thesis presents a novel approach for generating truly random num- bers in 5G wireless communication systems using the radio frequency (RF) spectrum. The proposed method leverages variations in the RF spectrum to create ... -
Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting
Kuzlu, Murat; Sarp, Salih; Catak, Ferhat Özgur; Cali, Umit; Zhao, Yanxiao; Elma, Onur; Guler, Ozgur (Peer reviewed; Journal article, 2022)The solar photovoltaics (PV) energy resources have become more important with their significant contribution to the current power grid among renewable energy resources. However, the integration of the solar PV causes ... -
Asymmetric Neural Cryptography with Homomorphic and Probabilistic Properties
Wøien, Mina Cecilie (Master thesis, 2024)Recent integrations of artificial intelligence in cryptography have improved traditional methods and known crypto attacks. However, the potential of combining neural cryptography with homomorphic and probabilistic encryption ... -
BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models
Wibawa, Febrianti; Catak, Ferhat Özgur; Sarp, Salih; Kuzlu, Murat (Peer reviewed; Journal article, 2022)Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The ... -
DDoS angrep generering for å utmatte GPU/CPU ressurser til IoT baserte AI applikasjoner
Myklebust, Benjamin Andre Scheiene; Anthonsen, Steffen Larsen (Bachelor thesis, 2023)I vår studie sikter vi på å teste dyktigheten til nettverksangrep deteksjonssystemer i Internet of Things enheter og deres egenskap til å motstå belastningsangrep på prosessorer gjennom et åpenkildet evolusjonær utviklingsprogram ... -
DDoS angrep generering for å utmatte GPU/CPU ressurser til IoT baserte AI applikasjoner
Anthonsen, Steffen Larsen; Myklebust, Benjamin Andre Scheiene (Bachelor thesis, 2023)I vår studie sikter vi på å teste dyktigheten til nettverksangrep deteksjonssystemer i Internet of Things enheter og deres egenskap til å motstå belastningsangrep på prosessorer gjennom et åpenkildet evolusjonær utviklingsprogram ... -
Defensive Distillation-based Adversarial Attack Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks
Catak, Ferhat Özgur; Kuzlu, Murat; Catak, Evren; Cali, Umit; Guler, Ozgur (Peer reviewed; Journal article, 2022-09)Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks ... -
Deployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids
Awan, Maaz Ali; Dalveren, Yaser; Catak, Ferhat Özgur; Kara, Ali (Peer reviewed; Journal article, 2023)Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart ... -
Developing and Testing Digital Twins for Vehicle Collision Prediction: A Machine Learning and Genetic Search Algorithm Approach
Kassaye, Yohannes Dawit; Hansen, Sigurd Grøvdal (Master thesis, 2023)This thesis focuses on developing a digital twin which can predict and avoid collisions. The digital twin does this by using different machine learning models that are trained on data from the SVL Simulator. By harnessing ... -
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples
Tuna, Omer Faruk; Catak, Ferhat Özgur; Eskil, Taner (Peer reviewed; Journal article, 2022)Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, ... -
Flexible and Lightweight Mitigation Framework for Distributed Denial-of-Service Attacks in Container-Based Edge Networks using Kubernetes
Koksal, Sarp; Catak, Ferhat Özgur; Dalveren, Yaser (Peer reviewed; Journal article, 2024)Mobile Edge Computing (MEC) has a significant potential to become more prevalent in Fifth Generation (5G) networks, requiring resource management that is lightweight, agile, and dynamic. Container-based virtualization ... -
Implemention and Evaluation of a Private Set Intersection Protocol Built With Fully Homomorphic Encryption
Nyland, Sarezh Paevl (Master thesis, 2023)Homomorphic encryption is a cryptographic technique which allows for computations on encrypted data. This type of cryptography helps respect the privacy of an entity that wishes to outsource computations on their data. ... -
Lattice-Based Cryptography for Privacy Preserving Machine Learning
Walskaar, Ivar; Tran, Minh Christian (Master thesis, 2023)The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is Federated Learning (FL), which enables ... -
Machine Learning for Tagging of Educational Content
Amundsen, Anne Helland (Master thesis, 2022)Online education has become a popular education form in recent years, with its use increasing massively during the COVID-19 pandemic. Neddy is a start-up company created at the start of the COVID-19 pandemic with the aim ... -
Modelling and Design of Pre-Equalizers for a Fully Operational Visible Light Communication System
Bostanoglu, Murat; Dalveren, Yaser; Catak, Ferhat Özgur; Kara, Ali (Peer reviewed; Journal article, 2023-06)Nowadays, Visible Light Communication (VLC) has gained much attention due to the significant advancements in Light Emitting Diode (LED) technology. However, the bandwidth of LEDs is one of the important concerns that limits ... -
Novel Data Encodings For Quantum Machine Learning: Enhancing the Quantum Feed-forward Neural Network for Improved Image Recognition
Haavardtun, Emil (Master thesis, 2023)Quantum machine learning combines the realms of quantum computing with artificial intelligence, providing novel approaches to problem-solving. Quantum image recognition is one such problem that has attracted significant ... -
On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection
Mohamed, Ismail; Dalveren, Yaser; Catak, Ferhat Özgur; Kara, Ali (Peer reviewed; Journal article, 2022-01)In the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible ... -
A Practical Implementation of Medical Privacy-Preserving Federated Learning Using Multi-Key Homomorphic Encryption and Flower Framework
Walskaar, Ivar; Tran, Minh Christian; Catak, Ferhat Özgur (Peer reviewed; Journal article, 2023-10)The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is federated learning, which enables ... -
Privacy of 5G Enabled Networks: Homomorphic Encryption based Privacy-Preserving Machine Learning
Pierzgalski, Emil Alan (Master thesis, 2023)Homomorphic encryption (HE) is a technique that allows computations to be performed on encrypted data, just as if the data were unencrypted. This has numerous potential applications, such as sensitive medical data, mainly ... -
Privacy-Preserving Machine Learning for Health Institutes
Wibawa, Febrianti (Master thesis, 2022)Medical data is, due to its nature, often susceptible to data privacy and security concerns. The identity of a person can be derived from medical data. Federated learning, one type of machine learning technique, is popularly ...