Saran, Ayşe Nurdan
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Saran, Nurdan Ayse
Saran, Nurdan
Saran, Ayse Nurdan
Saran, Nurdan Buz
Buz, Ayşe Nurdan
Saran, Nurdan
Saran, Ayse Nurdan
Saran, Nurdan Buz
Buz, Ayşe Nurdan
Job Title
Dr. Öğr. Üyesi
Email Address
buz@cankaya.edu.tr
Main Affiliation
Bilgisayar Mühendisliği
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Current Staff
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Sustainable Development Goals
1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Documents
15
Citations
79
h-index
6
Documents
14
Citations
49
Scholarly Output
32
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17
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3399/1174
Supervised MSc Theses
4
Supervised PhD Theses
0
WoS Citation Count
49
Scopus Citation Count
85
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0
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0
WoS Citations per Publication
1.53
Scopus Citations per Publication
2.66
Open Access Source
14
Supervised Theses
4
| Journal | Count |
|---|---|
| PeerJ Computer Science | 6 |
| Turkish Journal of Electrical Engineering and Computer Sciences | 2 |
| 2017 25th Signal Processing And Communications Applications Conference (SIU) | 1 |
| 25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY | 1 |
| 29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK | 1 |
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32 results
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Now showing 1 - 10 of 32
Article Cassandra ve MongoDB NoSQL Veri Tabanlarının Karşılaştırmalı Güvenlik Analizi(2019) Saran, Murat; Saran, NurdanIn this study, we analyze the security of two NoSQL databases, MongoDB 3.6.3 and Cassandra 3.11.1 in a multi-node configuration in two steps. The first step is a comparative study of both databases’ security features according to ten selected criteria from the literature. The second step is analyzing data encryption overhead using the Yahoo Cloud Serving Benchmark tool. This study will help decision-makers and researchers to realize the most crucial security features concerning NoSQL databases as well as to be able to analyze the NoSQL databases regarding the security features. Our security comparison results show that both databases have noteworthy security features. However, Cassandra takes the lead as it supports more security criteria. Besides, the encryption/decryption performance of the MongoDB business version is 53% faster than the Cassandra business version, and the average amount of data that the MongoDB business version can process per minute is 45% higher than the Cassandra business version. This result shows that it is more appropriate to use MongoDB in environments where encryption is required.Book Part Parallelization of sparsity-driven change detection method(IEEE, 2017) Özgür, Atilla; Saran, Ayşe Nurdan; Nar, FatihIn this study, Sparsity-driven Change Detection (SDCD) method, which has been proposed for detecting changes in multitemporal synthetic aperture radar (SAR) images, is parallelized to reduce the execution time. Parallelization of the SDCD is realized using OpenMP on CPU and CUDA on GPU. Execution speed of the parallelized SDCD is shown on real-world SAR images. Our experimental results show that the computation time of the parallel implementation brings significant speed-ups.Publication New distinguishers based on random mappings against stream ciphers(Springer-Verlag Berlin, 2008) Turan, Meltem Sönmez; Çalık, Çağdaş; Saran, Nurdan Buz; Doğanaksoy, AliStatistical randomness testing plays an important role in security analysis of cryptosystems. In this study, we aim to propose a new framework of randomness testing based on random mappings. Considering the probability distributions of coverage and P-lengths, we present three new distinguishers; (i) coverage test, (ii) p-test and (iii) DP-coverage test and applied them on Phase III Candidates of eSTREAM project. We experimentally observed some statistical weaknesses of Po-maranch using the coverage test.Article Citation - WoS: 13Citation - Scopus: 15Sparsity-Driven Change Detection in Multitemporal Sar Images(Ieee-inst Electrical Electronics Engineers inc, 2016) Saran, Ayse Nurdan; Nar, Fatih; Ozgur, AtillaIn this letter, a method for detecting changes in multitemporal synthetic aperture radar (SAR) images by minimizing a novel cost function is proposed. This cost function is constructed with log-ratio-based data fidelity terms and an l(1)-norm-based total variation (TV) regularization term. Log-ratio terms model the changes between the two SAR images where the TV regularization term imposes smoothness on these changes in a sparse manner such that fine details are extracted while effects like speckle noise are reduced. The proposed method, sparsity-driven change detection (SDCD), employs accurate approximation techniques for the minimization of the cost function since data fidelity terms are not convex and the employed l(1)-norm TV regularization term is not differentiable. The performance of the SDCD is shown on real-world SAR images obtained from various SAR sensors.Article Citation - WoS: 2Citation - Scopus: 4On Time-Memory Trade-Offs for Password Hashing Schemes(Frontiers Media Sa, 2024) Saran, Ayse NurdanA password hashing algorithm is a cryptographic method that transforms passwords into a secure and irreversible format. It is used not only for authentication purposes but also for key derivation mechanisms. The primary purpose of password hashing is to enhance the security of user credentials by preventing the exposure of plaintext passwords in the event of a data breach. As a key derivation function, password hashing aims to derive secret keys from a master key, password, or passphrase using a pseudorandom function. This review focuses on the design and analysis of time-memory trade-off (TMTO) attacks on recent password hashing algorithms. This review presents a comprehensive survey of TMTO attacks and recent studies on password hashing for authentication by examining the literature. The study provides valuable insights and strategies for safely navigating transitions, emphasizing the importance of a systematic approach and thorough testing to mitigate risk. The purpose of this paper is to provide guidance to developers and administrators on how to update cryptographic practices in response to evolving security standards and threats.Conference Object Parallelization of Sparsity-Driven Change Detection Method(Ieee, 2017) Ozgur, Atilla; Saran, Ayse Nurdan; Nar, FatihIn this study, Sparsity-driven Change Detection (SDCD) method, which has been proposed for detecting changes in multitemporal synthetic aperture radar (SAR) images, is parallelized to reduce the execution time. Parallelization of the SDCD is realized using OpenMP on CPU and CUDA on GPU. Execution speed of the parallelized SDCD is shown on real-world SAR images. Our experimental results show that the computation time of the parallel implementation brings significant speed-ups.Article Distribution-Preserving Data Augmentation(PeerJ Inc., 2021) Saran, Nurdan Ayse; Nar, Fatih; Saran, MuratArticle Citation - WoS: 2Citation - Scopus: 6Fast Binary Logistic Regression(Peerj inc, 2025) Saran, Nurdan Ayse; Nar, FatihThis study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. We also adopt the L-f-norm penalty, which allows using fractional norms, including the L-2-norm, L-1-norm, and L-0-norm, to regularize the model parameters. We put L-f-norm formulation in matrix-vector form, providing flexibility to include or exclude penalization of the intercept term when applying regularization. Furthermore, to address the common problem of collinear features, we apply singular value decomposition (SVD), resulting in a low-rank representation commonly used to reduce computational complexity while preserving essential features and mitigating noise. Moreover, our approach incorporates a randomized SVD alongside a newly developed SVD with row reduction (SVD-RR) method, which aims to manage datasets with many rows and features efficiently. This computational efficiency is crucial in developing a generalized model that requires repeated training over various parameters to balance bias and variance. We also demonstrate the effectiveness of our fast binary logistic regression (FBLR) method on various datasets from the OpenML repository in addition to synthetic datasets.Article Citation - WoS: 5Citation - Scopus: 5Vessel Segmentation in Mri Using a Variational Image Subtraction Approach(2014) Saran, Ayşe Nurdan; Nar, Fatih; Saran, MuratVessel segmentation is important for many clinical applications, such as the diagnosis of vascular diseases, the planning of surgery, or the monitoring of the progress of disease. Although various approaches have been proposed to segment vessel structures from 3-dimensional medical images, to the best of our knowledge, there has been no known technique that uses magnetic resonance imaging (MRI) as prior information within the vessel segmentation of magnetic resonance angiography (MRA) or magnetic resonance venography (MRV) images. In this study, we propose a novel method that uses MRI images as an atlas, assuming that the patient has an MRI image in addition to MRA/MRV images. The proposed approach intends to increase vessel segmentation accuracy by using the available MRI image as prior information. We use a rigid mutual information registration of the MRA/MRV to the MRI, which provides subvoxel accurate multimodal image registration. On the other hand, vessel segmentation methods tend to mostly suffer from imaging artifacts, such as Rician noise, radio frequency (RF) inhomogeneity, or partial volume effects that are generated by imaging devices. Therefore, this proposed method aims to extract all of the vascular structures from MRA/MRI or MRV/MRI pairs at the same time, while minimizing the combined effects of noise and RF inhomogeneity. Our method is validated both quantitatively and visually using BrainWeb phantom images and clinical MRI, MRA, and MRV images. Comparison and observer studies are also realized using the BrainWeb database and clinical images. The computation time is markedly reduced by developing a parallel implementation using the Nvidia compute unified device architecture and OpenMP frameworks in order to allow the use of the method in clinical settings.Master Thesis Gizliliği Koruyan Federated Öğrenme ile Giriş Tespitini Geliştirme: Farklı Mahremiyet ve Artırımlı Öğrenme Entegrasyonu(2025) Asal, Ali Sadeq Hussein; Saran, Ayşe NurdanSiber güvenlikte, Saldırı Tespit Sistemleri (IDS), ağları ve sistemleri kötü niyetli faaliyetleri tespit etmek için tarar ve hassas veriler tehlikeye girmeden tehditleri tanımlamaya yardımcı olur. Makine öğreniminin (ML) tanıtılması, IDS'yi otomatik ve akıllı tehdit algılama mekanizmaları sağlayarak geliştirmiştir. Ancak, Federated Learning (FL) gibi dağıtılmış ortamlarda ML modellerinin eğitimi, model parametrelerinin analizi yoluyla hassas bilgileri açığa çıkarabilir. FL, verileri yerelleştirerek belirli gizlilik sorunlarını hafifletir, ancak gerçek bir gizlilik koruması için yeterli değildir ve geliştirilmesi gereklidir. Özellikle, Artırımlı Öğrenme (IL), IDS'yi yeniden eğitime ihtiyaç duymadan yeni siber güvenlik tehditlerine uyum sağlama yeteneği sunarak iyileştirir. Bu, hesaplama açısından maliyeti düşük tutar ve yeni saldırı davranışlarına hızla uyum sağlar. Özellikle, Federated Differential Privacy Enhanced Model Aggregation adlı bir yöntem öneriyoruz; bu yöntem, federated ML bağlamında hem gizliliği hem de doğruluğu artırmayı hedeflemektedir. Bu yöntem, bir global modelin başlatıldığı ve istemci tarafında eğitimle daha da geliştirildiği bir sunucu-istemci mimarisi kullanır ve güncellemeler güvenli bir şekilde birleştirilir. Ayrıca, veri gizliliğini artırmak için gradyanlara gürültü ekleyen DP-SGD optimizasyonuyla eğitilmiş çok katmanlı bir algılayıcı (MLP) kullandık. Performansı değerlendirdik ve deneysel sonuçlar, önerdiğimiz yaklaşımda sınıf artımlı öğrenmenin doğruluğunun %92,4'e ve özellik artımlı öğrenmenin %99,4'e ulaştığını göstermektedir. Bu sonuçlar, modelimizin yeni verileri iyi bir şekilde öğrenebildiğini ortaya koymaktadır. Süreç, gizliliği koruyucu ve verimli kalmakta olup farklı veri kümesi türleri üzerinde iyi performans sergilemektedir. Bu nedenle, modern çağda bir saldırı tespit sistemi (IDS) için geçerli bir aday olduğuna inanıyoruz.
