Hasan Dağ

Dağ, Hasan

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

hasan.dag@khas.edu.tr

Main Affiliation

Management Information Systems

Sustainable Development Goals Report Points

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Scholarly Output

85

Articles

17

Citation Count

238

Supervised Theses

26 Scholarly Output Search Results

Now showing 1 - 10 of 17

Citation - WoS: 28
Citation - Scopus: 43
An ensemble of pre-trained transformer models for imbalanced multiclass malware classification
(Elsevier Advanced Technology, 2022) Dağ, Hasan; Demirkıran, Ferhat; Unal, Gur; Dag, Hasan; Management Information Systems
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.
Citation - WoS: 61
Citation - Scopus: 60
A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models
(Springer, 2024) Dağ, Hasan; Jafari Navimipour, Nima; Dag, Hasan; Talebi, Samira; Unal, Mehmet; Computer Engineering; Management Information Systems
In recent years, the proliferation of deep learning (DL) techniques has given rise to a significant challenge in the form of deepfake videos, posing a grave threat to the authenticity of media content. With the rapid advancement of DL technology, the creation of convincingly realistic deepfake videos has become increasingly prevalent, raising serious concerns about the potential misuse of such content. Deepfakes have the potential to undermine trust in visual media, with implications for fields as diverse as journalism, entertainment, and security. This study presents an innovative solution by harnessing blockchain-based federated learning (FL) to address this issue, focusing on preserving data source anonymity. The approach combines the strengths of SegCaps and convolutional neural network (CNN) methods for improved image feature extraction, followed by capsule network (CN) training to enhance generalization. A novel data normalization technique is introduced to tackle data heterogeneity stemming from diverse global data sources. Moreover, transfer learning (TL) and preprocessing methods are deployed to elevate DL performance. These efforts culminate in collaborative global model training zfacilitated by blockchain and FL while maintaining the utmost confidentiality of data sources. The effectiveness of our methodology is rigorously tested and validated through extensive experiments. These experiments reveal a substantial improvement in accuracy, with an impressive average increase of 6.6% compared to six benchmark models. Furthermore, our approach demonstrates a 5.1% enhancement in the area under the curve (AUC) metric, underscoring its ability to outperform existing detection methods. These results substantiate the effectiveness of our proposed solution in countering the proliferation of deepfake content. In conclusion, our innovative approach represents a promising avenue for advancing deepfake detection. By leveraging existing data resources and the power of FL and blockchain technology, we address a critical need for media authenticity and security. As the threat of deepfake videos continues to grow, our comprehensive solution provides an effective means to protect the integrity and trustworthiness of visual media, with far-reaching implications for both industry and society. This work stands as a significant step toward countering the deepfake menace and preserving the authenticity of visual content in a rapidly evolving digital landscape.
Citation - WoS: 7
Citation - Scopus: 9
Anomalyadapters: Parameter-Efficient Multi-Anomaly Task Detection
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Unal, Ugur; Dağ, Hasan; Dag, Hasan; Management Information Systems
The emergence of technological innovations brings sophisticated threats. Cyberattacks are increasing day by day aligned with these innovations and entails rapid solutions for defense mechanisms. These attacks may hinder enterprise operations or more importantly, interrupt critical infrastructure systems, that are essential to safety, security, and well-being of a society. Anomaly detection, as a protection step, is significant for ensuring a system security. Logs, which are accepted sources universally, are utilized in system health monitoring and intrusion detection systems. Recent developments in Natural Language Processing (NLP) studies show that contextual information decreases false-positives yield in detecting anomalous behaviors. Transformers and their adaptations to various language understanding tasks exemplify the enhanced ability to extract this information. Deep network based anomaly detection solutions use generally feature-based transfer learning methods. This type of learning presents a new set of weights for each log type. It is unfeasible and a redundant way considering various log sources. Also, a vague representation of model decisions prevents learning from threat data and improving model capability. In this paper, we propose AnomalyAdapters (AAs) which is an extensible multi-anomaly task detection model. It uses pretrained transformers' variant to encode a log sequences and utilizes adapters to learn a log structure and anomaly types. Adapter-based approach collects contextual information, eliminates information loss in learning, and learns anomaly detection tasks from different log sources without overuse of parameters. Lastly, our work elucidates the decision making process of the proposed model on different log datasets to emphasize extraction of threat data via explainability experiments.
Citation - WoS: 0
Citation - Scopus: 0
Mathematical Foundations and Implementation of Coniks Key Transparency
(Mdpi, 2024) Dağ, Hasan; Dag, Hasan; Dimitrova, Vesna; Management Information Systems
This research paper explores the CONIKS key management system's security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.
Citation - WoS: 0
A Sparsity-Preserving Spectral Preconditioner for Power Flow Analysis
(TUBITAK Scientific & Technical Research Council Turkey, 2016) Yetkin, Emrullah Fatih; Yetkin, Emrullah Fatih; Dağ, Hasan; Dağ, Hasan; Business Administration; Management Information Systems
Due to the ever-increasing demand for more detailed and accurate power system simulations the dimensions of mathematical models increase. Although the traditional direct linear equation solvers based on LU factorization are robust they have limited scalability on the parallel platforms. On the other hand simulations of the power system events need to be performed at a reasonable time to assess the results of the unwanted events and to take the necessary remedial actions. Hence to obtain faster solutions for more detailed models parallel platforms should be used. To this end direct solvers can be replaced by Krylov subspace methods (conjugate gradient generalized minimal residuals etc.). Krylov subspace methods need some accelerators to achieve competitive performance. In this article a new preconditioner is proposed for Krylov subspace-based iterative methods. The proposed preconditioner is based on the spectral projectors. It is known that the computational complexity of the spectral projectors is quite high. Therefore we also suggest a new approximate computation technique for spectral projectors as appropriate eigenvalue-based accelerators for efficient computation of power flow problems. The convergence characteristics and sparsity structure of the preconditioners are compared to the well-known black-box preconditioners such as incomplete LU and the results are presented.
Citation - WoS: 5
Citation - Scopus: 5
Heuristic Methods for Postoutage Voltage Magnitude Calculations
(TUBITAK Scientific & Technical Research Council Turkey, 2016) Dağ, Hasan; Ceylan, Oğuzhan; Dağ, Hasan; Management Information Systems
Power systems play a significant role in every aspect of our daily lives. Hence their continuation without any interruption (or with the least duration of interruption due to faults or scheduled maintenances) is one of the key aims of electrical energy providers. As a result electrical energy providers need to check in great detail the integrity of their power systems by performing regular contingency studies of the equipment involved. Among others line and transformer outage simulations constitute an integral part of an electrical management system. Both accuracy and calculation speed depend on the branch outage model and/or the solution algorithms applied. In this paper the local constrained optimization problem of the single-branch outage problem is solved by intelligent methods: particle swarm optimization differential evolution and harmony search. Simulations of IEEE 14- 30- 118- and 300 -bus systems are computed both by intelligent methods and by AC load flow. The results of the intelligent method -based simulations and AC load flow -based simulations are compared in terms of accuracy and computation speed.
Citation - WoS: 3
Citation - Scopus: 6
Machine Learning Model To Predict an Adult Learner's Decision To Continue Esol Course or Not
(Springer, 2019) Dahman, Mohammed R.; Dağ, Hasan; Dağ, Hasan; Management Information Systems
This study investigated the ability of the demographic and the affective variables to predict the adult learners' decision to continue ESOL courser. 278 adult learners, enrolled on ESOL course at FLS institution in Istanbul, Turkey, participated in the study. The result showed that the continued or dropped out groups, demonstrated statistical differences in the demographic variable (the placement test score) with a magnitude of large effect size (.378). Additionally, the result showed the effect size in the perception of the affective variables (motivation, attitude, and anxiety), accounts for about 50% of the variation between the continuation and dropout groups. Following that, three machine learning models were proposed; all possible subset regression analysis was used to compare the three models. The adequate model, which fitted the demographic variable (the placement test score) and the affective variables (motivation, attitude, and anxiety), correctly predicted 83.3% of the adult learners' decision to continue ESOL course. The model showed about 68% goodness-of-fit. The cultural implications of these findings are discussed, along with suggestions for future research.
Citation - WoS: 2
Citation - Scopus: 6
Branch Outage Simulation Based Contingency Screening by Gravitational Search Algorithm
(Praise Worthy Prize Srl, 2012) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Özdemir, Aydoğan; Dağ, Hasan; Dağ, Hasan; Özdemir, Serpil; Advertising; Management Information Systems
Power systems contingency analysis is an important issue for electric power system operators. This paper performs branch outage simulation based contingency screening using a bounded network approach. Local constrained optimization problem representing the branch outage phenomena is solved by the gravitational search algorithm. The proposed method is applied to IEEE 14 30 57 and 118 Bus Test systems and its performance from the point of capturing violations is evaluated. In addition false alarms and the computational accuracy of the proposed method are also analyzed by using scattering diagrams. Finally the proposed gravitational search based contingency screening is compared with full AC load flow solutions from the point of computational speed. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Double Branch Outage Modeling and Simulation: Bounded Network Approach
(Elsevier Science, 2015) Ceylan, Oğuzhan; Dağ, Hasan; Özdemir, Aydoğan; Ceylan, Oğuzhan; Dağ, Hasan; Özdemir, Serpil; Advertising; Management Information Systems
Energy management system operators perform regular outage simulations in order to ensure secure operation of power systems. AC power flow based outage simulations are not preferred because of insufficient computational speed. Hence several outage models and computational methods providing acceptable accuracy have been developed. On the other hand double branch outages are critical rare events which can result in cascading outages and system collapse. This paper presents a double branch outage model and formulation of the phenomena as a constrained optimization problem. Optimization problem is then solved by using differential evolution method and particle swarm optimization algorithm. The proposed algorithm is applied to IEEE test systems. Computational accuracies of differential evolution based solutions and particle swarm optimization based solutions are discussed for IEEE 30 Bus Test System and IEEE 118 Bus Test System applications. IEEE 14 Bus Test System IEEE 30 Bus Test System IEEE 57 Bus Test System IEEE 118 Bus Test System and IEEE 300 Bus Test System simulation results are compared to AC load flows in terms of computational speed. Finally the performance of the proposed method is analyzed for different outage configurations. (C) 2015 Elsevier Ltd. All rights reserved.
Citation - Scopus: 41
Random Capsnet Forest Model for Imbalanced Malware Type Classification Task
(Elsevier, 2021) Çayır, Aykut; Dağ, Hasan; Ünal, Uğur; Dağ, Hasan; Management Information Systems
Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracies 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 paper 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.