Merve GÜLMEZ

Merve GÜLMEZ

Researcher

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Pages

Posts

security

less than 1 minute read

Published:

At the beginning of my PhD I at first became interested in the broader area of cybersecurity, but my current line of research really got me into the area of systems security. In particular improving the cyber-resilience and availability low-level systems software against memory corruption attacks. This has required me to develop skills, such as in-depth knowledge of the C and Rust runtimes, as well as assembly programming.

crowdstrike

1 minute read

Published:

The CrowdStrike outage shows that improving software resilience and availability is as important as detecting and mitigating memory-safety vulnerabilities.

LLVM

1 minute read

Published:

I have been learning LLVM for a while; I added a function and variable attribute, new compiler options, wrote a hacky LLVM transformation pass, and deleted my hacky stuff in the backend! I kind of enjoyed it, kind of annoyed; in the end, I think it was good to learn!

Bluspec

less than 1 minute read

Published:

To install the BSC compiler, follow these steps (example for Ubuntu):

Example for Ubuntu

wget https://github.com/B-Lang-org/bsc/releases/download/2023.07/bsc-2023.07-ubuntu-22.04.tar.gz 
cd ~/cheri/bsc-2023.07-debian-12.1 
export PATH="$PATH:/home/cheri/bsc-2023.07-ubuntu-22.04/bin/

It’s likely that you will also need the Bluespec library. It took me a long time to understand what was missing. You need to install this library from the following repository:

https://github.com/B-Lang-org/bsc-contrib/tree/main

CHERI

less than 1 minute read

Published:

Adversarial Examples: A great starting point! These exercises will introduce you to the concept of capabilities and how they can be used to protect against various security threats.

RUST

less than 1 minute read

Published:

I worked on macro expansion on Rust for that project: https://arxiv.org/pdf/2306.08127

GDB

3 minute read

Published:

I am studying on low-level security.

portfolio

publications

Deep convolutional learning-aided detector for generalized frequency division multiplexing with index modulation

Published in 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019

In this paper, a deep convolutional neural network-based symbol detection and demodulation is pro- posed for generalized frequency division multiplexing with index modulation (GFDM-IM) scheme in order to improve the error performance of the system. The proposed method first pre-processes the received signal by using a zero- forcing (ZF) detector and then uses a neural network con- sisting of a convolutional neural network (CNN) followed by a fully-connected neural network (FCNN). The FCNN part uses only two fully-connected layers, which can be adapted to yield a trade-off between complexity and bit error rate (BER) performance. This two-stage approach prevents the getting stuck of neural network in a saddle point and enables IM blocks processing independently. It has been demonstrated that the proposed deep convolutional neural network-based detection and demodulation scheme provides better BER performance compared to ZF detector with a reasonable complexity increase. We conclude that non-orthogonal waveforms combined with IM schemes with the help of deep learning is a promising physical layer (PHY) scheme for future wireless networks.

Deep Learning-aided Spatial Multiplexing with Index Modulation

Published in International Conference on Machine Learning for Networking, 2020

In this paper, deep learning (DL)-aided data detection of spatial multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been constructed by combining a zero-forcing (ZF) detector and DL technique. The proposed method uses the significant advantages of DL techniques to learn transmission characteristics of the frequency and spatial domains. Furthermore, thanks to using subblock-based detection provided by IM, Deep-SMX-IM is a straightforward method, which eventually reveals reduced complexity. It has been shown that Deep-SMX-IM has significant error performance gains compared to ZF detector without increasing computational complexity for different system configurations.