Search Results for author: Ferhat Ozgur Catak

Found 15 papers, 6 papers with code

Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals

no code implementations27 Sep 2022 Ferhat Ozgur Catak, Murat Kuzlu, Salih Sarp, Evren Catak, Umit Cali

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 technologies.

Management Semantic Segmentation

Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data

no code implementations21 Sep 2022 Yuval Abraham Regev, Henrik Vassdal, Ugur Halden, Ferhat Ozgur Catak, Umit Cali

Over the last few decades, extensive use of information and communication technologies has been the main driver of the digitalization of power systems.

Anomaly Detection

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

no code implementations16 Feb 2022 Murat Kuzlu, Ferhat Ozgur Catak, Umit Cali, Evren Catak, Ozgur Guler

This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks (DNNs) in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem.

Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness

1 code implementation15 Feb 2022 Omer Faruk Tuna, Ferhat Ozgur Catak, M. Taner Eskil

In this study, we show both mathematically and experimentally that using some widely known activation functions in the output layer of the model with high temperature values has the effect of zeroing out the gradients for both targeted and untargeted attack cases, preventing attackers from exploiting the model's loss function to craft adversarial samples.

Adversarial Robustness

Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems

no code implementations29 Sep 2021 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 health sector and improves daily human life.

Federated Learning Privacy Preserving

Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving

1 code implementation11 Jul 2021 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 decision making for object recognition, adjusting speed, etc.

Autonomous Driving Decision Making +4

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction

no code implementations9 May 2021 Ferhat Ozgur Catak, Evren Catak, Murat Kuzlu, Umit Cali, Devrim Unal

We also present the adversarial learning mitigation method's performance for 6G security in mmWave beam prediction application with fast gradient sign method attack.

BIG-bench Machine Learning

Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case

no code implementations12 Mar 2021 Evren Catak, Ferhat Ozgur Catak, Arild Moldsvor

This paper has proposed a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction with adversarial learning.

BIG-bench Machine Learning

Internet of Predictable Things (IoPT) Framework to Increase Cyber-Physical System Resiliency

no code implementations19 Jan 2021 Umit Cali, Murat Kuzlu, Vinayak Sharma, Manisa Pipattanasomporn, Ferhat Ozgur Catak

During the last two decades, distributed energy systems, especially renewable energy sources (RES), have become more economically viable with increasing market share and penetration levels on power systems.

Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine Learning

1 code implementation11 Dec 2020 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 deliberately crafted perturbations, albeit being quasi-imperceptible.

BIG-bench Machine Learning

Data Augmentation Based Malware Detection using Convolutional Neural Networks

1 code implementation5 Oct 2020 Ferhat Ozgur Catak, Javed Ahmed, Kevser Sahinbas, Zahid Hussain Khand

The main contributions of the paper's model structure consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a convolutional neural network model.

Image Augmentation Image Generation +2

A Benchmark API Call Dataset for Windows PE Malware Classification

3 code implementations6 May 2019 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.

Cryptography and Security

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