Dağ, Hasan

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12469/6106
Name Variants
D., Hasan
Dağ, HASAN
DAĞ, HASAN
Hasan, Dag
HASAN DAĞ
Hasan DAĞ
DAĞ, Hasan
Daǧ H.
Hasan Dağ
Dağ, H.
Dağ,H.
D.,Hasan
Dağ, Hasan
Dag H.
Dag,H.
Dağ H.
Dag,Hasan
Dag, Hasan
H. Dağ
Da?, Hasan
Job Title
Prof. Dr.
Email Address
[email protected]
Main Affiliation
Management Information Systems
Status
Current Staff
Website
ORCID ID
0000-0001-6252-1870
Scopus Author ID
6507328166
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
T-5301-2018 

Sustainable Development Goals

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

2

Research Products

10

REDUCED INEQUALITIES
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0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

16

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

Research Products

2

ZERO HUNGER
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0

Research Products

3

GOOD HEALTH AND WELL-BEING
GOOD HEALTH AND WELL-BEING Logo

2

Research Products

13

CLIMATE ACTION
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0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
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2

Research Products

5

GENDER EQUALITY
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0

Research Products

6

CLEAN WATER AND SANITATION
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0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
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2

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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0

Research Products

4

QUALITY EDUCATION
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2

Research Products

15

LIFE ON LAND
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3

Research Products

1

NO POVERTY
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0

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14

LIFE BELOW WATER
LIFE BELOW WATER Logo

1

Research Products

17

PARTNERSHIPS FOR THE GOALS
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3

Research Products
Documents

85

Citations

946

h-index

13

Go to Scopus profile
Documents

45

Citations

225

Go to WoS profile
Scholarly Output

101

Articles

19

Views / Downloads

98/0

Supervised MSc Theses

26

Supervised PhD Theses

3

WoS Citation Count

448

Scopus Citation Count

859

WoS h-index

8

Scopus h-index

12

Patents

0

Projects

0

WoS Citations per Publication

4.44

Scopus Citations per Publication

8.50

Open Access Source

53

Supervised Theses

29

JournalCount
Computers & Security2
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES2
2010 International Conference on Power System Technology2
UBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineering -- 8th International Conference on Computer Science and Engineering, UBMK 2023 -- 13 September 2023 through 15 September 2023 -- Burdur -- 1938732
UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering -- 9th International Conference on Computer Science and Engineering, UBMK 2024 -- 26 October 2024 through 28 October 2024 -- Antalya -- 2049062
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Now showing 1 - 10 of 101
  • Thumbnail Image
    Article
    Citation - WoS: 44
    Citation - Scopus: 62
    An ensemble of pre-trained transformer models for imbalanced multiclass malware classification
    (Elsevier Advanced Technology, 2022) Demirkiran, Ferhat; Cayir, Aykut; Unal, Gur; Dag, Hasan; Ünal, Uğur
    Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Hence, malware identification enables security researchers and incident responders to take precautions against malware and accelerate mitigation. API call sequences made by malware are widely utilized features by machine and deep learning models for malware classification as these sequences represent the behavior of malware. However, traditional machine and deep learning models remain incapable of capturing sequence relationships among API calls. Unlike traditional machine and deep learning models, the transformer-based models process the sequences in whole and learn relationships among API calls due to multi-head attention mechanisms and positional embeddings. Our experiments demonstrate that the Transformer model with one transformer block layer surpasses the performance of the widely used base architecture, LSTM. Moreover, BERT or CANINE, the pre-trained transformer models, outperforms in classifying highly imbalanced malware families according to evaluation metrics: F1-score and AUC score. Furthermore, our proposed bagging-based random transformer forest (RTF) model, an ensemble of BERT or CANINE, reaches the state-of-the-art evaluation scores on the three out of four datasets, specifically it captures a state-of-the-art F1-score of 0.6149 on one of the commonly used benchmark dataset. (C) 2022 Elsevier Ltd. All rights reserved.
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    Conference Object
    On the Selection of Interpolation Points for Rational Krylov Methods
    (Springer-Verlag Berlin, 2012) Yetkin, E. Fatih; Dağ, Hasan
    We suggest a simple and an efficient way of selecting a suitable set of interpolation points for the well-known rational Krylov based model order reduction techniques. To do this some sampling points from the frequency response of the transfer function are taken. These points correspond to the places where the sign of the numerical derivation of transfer function changes. The suggested method requires a set of linear system's solutions several times. But they can be computed concurrently by different processors in a parallel computing environment. Serial performance of the method is compared to the well-known H-2 optimal method for several benchmark examples. The method achieves acceptable accuracies (the same order of magnitude) compared to that of H-2 optimal methods and has a better performance than the common selection procedures such as linearly distributed points.
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    Conference Object
    Citation - Scopus: 5
    Double Branch Outage Modeling and Its Solution Using Differential Evolution Method
    (2011) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
    Power system operators need to check the system security by contingency analysis which requires power flow solutions repeatedly. AC power flow is computationally slow even for a moderately sized system. Thus fast and accurate outage models and approximated solutions have been developed. This paper adopts a single branch outage model to a double branch outage one. The final constrained optimization problem resulted from modeling is then solved by using differential evolution method. Simulation results for IEEE 30 and 118 bus test systems are presented and compared to those of full AC load flow in terms of solution accuracy. © 2011 IEEE.
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    Conference Object
    Power Consumption Estimation using In-Memory Database Computation
    (Ieee, 2016) Dag, Hasan; Alamin, Mohamed
    In order to efficiently predict electricity consumption, we need to improve both the speed and the reliability of computational environment. Concerning the speed, we use inmemory database, which is taught to be the best solution that allows manipulating data many times faster than the hard disk. For reliability, we use machine learning algorithms. Since the model performance and accuracy may vary depending on data each time, we test many algorithms and select the best one. In this study, we use SmartMeter Energy Consumption Data in London Households to predict electricity consumption using machine learning algorithms written in Python programming language and in-memory database computation package, Aerospike. The test results show that the best algorithm for our data set is Bagging algorithm. We also emphatically prove that R-squared may not always be a good test to choose the best algorithm.
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    Doctoral Thesis
    Random Capsule Network (capsnet) Forest Model for Imbalanced Malware Type Classification Task
    (Kadir Has Üniversitesi, 2021) Çayır, Aykut; Dağ, Hasan
    Behavior of malware varies depending the malware types, which affect the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracy for predicting malware types. Machine learning-based models need to do heavy feature engineering work, which affects the performance of the models greatly. On the other hand, deep learning-based models require less effort in feature engineering when compared to that of the machine learning-based models. However, traditional deep learning architectures' components, such as max and average pooling, cause architecture to be more complex and the models to be more sensitive to data. The capsule network architectures, on the other hand, reduce the aforementioned complexities by eliminating the pooling components. Additionally, capsule network architectures based models are less sensitive to data, unlike the classical convolutional neural network architectures. This thesis proposes an ensemble capsule network model based on the bootstrap aggregating technique. The proposed method is tested on two widely used, highly imbalanced datasets (Malimg and BIG2015), for which the-state-of-the-art results are well-known and can be used for comparison purposes. The proposed model achieves the highest F-Score, which is 0.9820, for the BIG2015 dataset and F-Score, which is 0.9661, for the Malimg dataset. Our model also reaches the-state-of-the-art, using 99.7% lower the number of trainable parameters than the best model in the literature.
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    Conference Object
    Citation - WoS: 3
    A Recommender Model Based on Trust Value and Time Decay Improve the Quality of Product Rating Score in E-Commerce Platforms
    (IEEE, 2017) Işık, Muhittin; Dağ, Hasan
    Most of the existing products rating score algorithms do not take fake accounts and time decay of users' ratings into account when creating the list of recommendations. The trust values and the time decay of users' ratings to an item may improve the quality of product rating score in e-commerce platforms especially when it is thought that nowadays the majority of customers read the reviews before making a purchase. In this paper we first introduce the concept trust value of users by explaining its mathematical definition and redefine the product rating score based on users' trust relationship. Then we calculate the product rating score based on time decay by making the concept time decay clear. After that we execute both algorithms together in order to show their both effects on the quality of product rating score. Finally we present experimentally effectiveness of three approaches on a large real dataset.
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    Conference Object
    Citation - Scopus: 2
    Comparison of Cost-Free Computational Tools for Teaching Physics
    (IEEE, 2010) Er, Neslihan Fatma; Dağ, Hasan
    It is widely accepted that it is quite difficult to engage today's students, from high schools to university, both in educational activities in class and "teaching" them physics due to their prejudices about the complexity of physics. The difficulty in capturing students' attention in class for a long time also plays a role in less effective teaching during learning activities. Research shows that students learn little from traditional lectures. According to constructivist learning theories, visual aids and hands-on activities play a major role in learning physics. In addition to laboratory work there are many computational tools for teaching physics, which help teachers and students in constructing a conceptual framework. With this in mind, this paper compares freeware and open source computational tools for teaching physics.
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    Conference Object
    Towards Faster Branch Outage Simulations Using Simulated Annealing and Parallel Programming
    (IEEE, 2009) Ceylan, Oğuzhan; Özdemir, Aydoğan; Dağ, Hasan
    Contingency studies such as branch outage and generator outage are among important studies of energy management centers operations. Branch outage modeling on the other hand is one of the basic steps of post-outage state estimation of an electrical power system. Real time implementation of the problem brings the necessity of using high speed methods while providing a reasonable accuracy. This paper presents simulated annealing based solution of the branch outage event which is formulated as a local optimization problem. To speed up the solution procedure the distributed computing toolbox of Matlab is used as a parallel programming tool. The results of the proposed method are compared to those of full AC method and are discussed both from the point of accuracy and solution speed.
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    Master Thesis
    Bulut Ortamlarında Güvenli Uygulama Dağıtımının Sağlanması
    (2025) Bostancı, Hakan; Dağ, Hasan
    Bulut bilişim, ölçeklenebilirlik, maliyet avantajı ve esneklik gibi faydaları nedeniyle modern BT altyapılarının ayrılmaz bir parçası haline gelmiştir. Organizasyonlar, operasyonel verimliliklerini artırmak, uzaktan çalışma süreçlerini desteklemek ve daha esnek iş modelleri oluşturmak için giderek daha fazla bulut çözümlerine yönelmektedir. Ancak, bu artan bağımlılık, güvenli uygulama teslimi açısından önemli güvenlik risklerini de beraberinde getirmektedir. Bulut bilişime olan adaptasyonun ve bağımlılığın artmasıyla birlikte, güvenli uygulama teslimini sağlamak kuruluşlar için kritik bir zorluk haline gelmiştir. Bulut ortamlarının dinamik yapısı ve gelişen siber tehditler, hassas verilerin korunması ve sistem bütünlüğünün sağlanması için güçlü güvenlik önlemlerini zorunlu kılmaktadır. Bu tez, bulut tabanlı uygulama tesliminde karşılaşılan temel güvenlik sorunlarını inceleyerek riskleri azaltmaya yönelik kapsamlı bir güvenlik yaklaşımı sunmaktadır.Çalışmada, uygulama güvenliği, API güvenliği, ağ güvenliği, veri şifreleme, kimlik ve erişim yönetimi ve gerçek zamanlı tehdit algılama mekanizmalarını entegre eden çok katmanlı bir güvenlik yaklaşımı benimsenmiştir. Bu yaklaşımda önerilen iyileştirmeler uygulamalı olarak gösterilmiştir. Microsoft Azure üzerinde dağıtılan bulut tabanlı bir e-ticaret uygulaması, güvenlik kontrollerinin uygulanması ve değerlendirilmesi için bir test ortamı olarak kullanılmıştır. Sistem direncini değerlendirmek için penetrasyon testleri, güvenlik açığı değerlendirmeleri ve DDoS ve saldırı simülasyonları gibi güvenlik test metodolojileri uygulanmıştır.Bulgular, geleneksel bulut dağıtımlarında önemli güvenlik açıklarının bulunduğunu ve proaktif güvenlik stratejilerinin gerekliliğini ortaya koymaktadır. Uygulama mimarisini güvenli hale getirme, ağ güvenliği, web uygulama güvenlik duvarı, ddos koruması, kimlik doğrulandırma ve yetkilendirme yönetimi gibi gelişmiş güvenlik önlemlerinin uygulanmasının ardından sistemin siber tehditlere karşı daha güçlü bir koruma sağladığı gözlemlenmiştir. Elde edilen sonuçlar, kuruluşların bulut tabanlı uygulamalarını etkili bir şekilde güvence altına almalarına yönelik uygulanabilir bilgiler sunarak bulut güvenliği alanına katkıda bulunmaktadır.
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    Conference Object
    Citation - Scopus: 1
    Transfer Learning for Phishing Detection: Screenshot-Based Website Classification
    (Institute of Electrical and Electronics Engineers Inc., 2024) Çolhak, F.; Ecevit, M.I.; Daǧ, H.
    Phishing remains a significant threat in the evolving cybersecurity landscape as phishing websites become increasingly similar to legitimate websites, complicating detection using traditional methods. This study explores AI-based solutions for screenshot-based phishing detection, utilizing the MTLP dataset and applying transfer learning with pretrained models (DenseNet, ResNet, EfficientNet, Inception, MobileNet, VGG) using the timm library. The study also discusses challenges related to phishing datasets and compares publicly available datasets, highlighting MTLP Dataset's strengths. DenseNetBlur121D was identified as the top-performing model, achieving an accuracy of 95.28%, a recall of 95.38%, a precision of 93.42%, and an F1 score of 94.39% when applied to the entire MTLP dataset. Both the model code and dataset are publicly available, providing a valuable resource for further research and development in this domain. © 2024 IEEE.