Person: BAYDOĞMUŞ, GÖZDE KARATAŞ
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Dr. Öğr. Üyesi
Last Name
BAYDOĞMUŞ
First Name
GÖZDE KARATAŞ
Name
17 results
Search Results
Now showing 1 - 10 of 17
Publication Survey on Access Control Mechanisms in Cloud Computing(River Publishers, 2018-05-26) AKBULUT, AKHAN; BAYDOĞMUŞ, GÖZDE KARATAŞ; 110942; 116056The benefits that Internet-based applications and services have given to the end user with today’s cloud computing technology are very remarkable. The distributed services instantly scaled over the Internet provided by cloud computing can be achieved by using some mechanisms in the background. It is a critical task for end users to control access to resources because lack of control often leads to security risks. In addition, this may cause systems to fail. This paper describes seven different access control mechanisms used in cloud computing platforms for different purposes. Besides, the advantages and disadvantages of various models developed from previous service-based architectures and used for cloud computing are detailed and classified. During the assessments, NIST’s metrics were taken as a reference, and in the study, 109 articles from the past decade were examined. We also compared our research with the existing survey papers.Publication Covid-19 Disease Detection with Improved Deep Learning Algorithms on X-Ray Data(Institute of Electrical and Electronics Engineers Inc., 2022) ÇİÇEKLİ, NAHİDE ZEYNEP; BAYDOĞMUŞ, GÖZDE KARATAŞThe COVID-19 pandemic has brought human life to a startling halt around the world from the moment it emerged and took thousands of lives. The health system has come to the point of collapse, many people in the world have died from being infected, and many people who have survived the disease have had permanent lung damage with the spread of COVID-19 in 212 countries and regions. In this study, an answer is sought to diagnose the disease-causing virus through Artificial Intelligence Algorithms. The aim of the study is to accelerate the diagnosis and treatment process of COVID-19 disease. Enhancements were made using Deep Learning methods, including CNN, VGG16, DenseNet121, and ResNet50. For this study, the disease was detected by using X-Ray images of patients with and without COVID-19 disease, and then it was evaluated how to increase the accuracy rate with the limited available data. To increase the accuracy rate, the results of data augmentation on the image data were examined and the time complexity of the algorithms with different layers was evaluated. As a result of the study, it was seen that data augmentation increased the performance rate in all algorithms and the ResNet50 algorithm was more successful than other algorithms. © 2022 IEEE.Publication Mobil Cihazlarda Güvenlik Tehditler Temel Stratejiler(Istanbul Commerce University, 2016-09-30) Zaim, Abdül Halim; AKBULUT, AKHAN; BAYDOĞMUŞ, GÖZDE KARATAŞ; 110942; 116056The most common form of today’s consumer electronics is the use of mobile devices. The technological developments in this area are increasing in such a way as to affect all aspects of our lives and give direction to our life. The use of mobile devices, which can now have the same hardware features as computers, is not only for communication but also enriched by applications in the areas of internet use, business, hobby and health. Increased usage rate and the need for information and communication security are beginning to be needed and it is necessary to ensure the security of the information carried against the attacks against these devices. Due to security vulnerabilities in mobile devices and malicious software loaded applications by end users, there are situations that threaten personal information and communication security. This study describes security vulnerabilities, attacks in mobile applications and precautions for those problems. It summarizes not only the end-user's recommendations, but also the points to note for app developers. It is evaluated that end users can increase personal security by learning basic info for attack methods of mobile systems.Publication A Systematic Mapping Study on Software Architecture Recovery(2016-11) Çatal, Çağatay; BAYDOĞMUŞ, GÖZDE KARATAŞ; ; 108363; 110942In this study, we investigated the approaches used in software architecture recovery papers, identify the current status of paper distributions in terms of year, publication channel, electronic databases, and journals. We executed a mapping study to cluster the software architecture recovery research papers. Papers published since 2000 have been used for this study. The following databases were investigated: Wiley, IEEE, ACM, Science Direct. Our search accessed 250 papers, but after in-depth analysis, 60 papers were found to be related to the software architecture recovery area. Our study shows that there exist many architecture recovery approaches in the literature, with machine learning-based techniques dominating the field. On the basis of this study, we suggest researchers develop more model centric software architecture recovery approaches because of model driven development’s popularity in software engineering field.Publication Deep Learning in Intrusion Detection Systems(2018) Demir, Önder; BAYDOĞMUŞ, GÖZDE KARATAŞ; ŞAHİNGÖZ, ÖZGÜR KORAY; 110942; 170651; 214903Publication Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset(IEEE, 2020) BAYDOĞMUŞ, GÖZDE KARATAŞ; Demir, Önder; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years, due to the extensive use of the Internet, the number of networked computers has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude on computers by using not only known but also new attack-types, which are more sophisticated and harder to detect. To protect the computers from them, Intrusion Detection System (IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don & x2019;t contain up-to-date data. Additionally, they are imbalanced and cannot hold sufficient data for all types of attacks. These imbalanced and outdated datasets decrease the efficiency of current IDSs, especially for rarely encountered attack types. In this paper, we propose six machine-learning-based IDSs by using K Nearest Neighbor, Random Forest, Gradient Boosting, Adaboost, Decision Tree, and Linear Discriminant Analysis algorithms. To implement a more realistic IDS, an up-to-date security dataset, CSE-CIC-IDS2018, is used instead of older and mostly worked datasets. The selected dataset is also imbalanced. Therefore, to increase the efficiency of the system depending on attack types and to decrease missed intrusions and false alarms, the imbalance ratio is reduced by using a synthetic data generation model called Synthetic Minority Oversampling TEchnique (SMOTE). Data generation is performed for minor classes, and their numbers are increased to the average data size via this technique. Experimental results demonstrated that the proposed approach considerably increases the detection rate for rarely encountered intrusions.Publication Saldırı Tespit Sistemlerinde Makine Öğrenmesi Modellerinin Karşılaştırılması(Erzincan Binali Yıldırım Üniversitesi, Fen Bilimleri Enstitüsü, 2019) ÇEBİ, CEM BERKE; BULUT, FATMA SENA; FIRAT, HAZAL; BAYDOĞMUŞ, GÖZDE KARATAŞ; ŞAHİNGÖZ, ÖZGÜR KORAYSon yıllardaki gelişen teknolojiler neticesinde her türlü hesaplama cihazının İnternete bağlanması sağlanmıştır. Bu sayede birçok gerçek dünya problemi yeni ağ düzenine aktarılsa da bu tam-kontrol sağlanamayan sanal platform çok sayıda güvenlik açığı içermektedir. Günümüzde ağ yöneticilerin ana görevlerinden biride bu açıkları kapatmak ve sorumlu oldukları bilgisayar ağını saldırılardan korumaktır. Güvenlik duvarlarının kullanımı dışarıdan yapılan saldırıları ciddi boyutta engellese de içeriden yapılabilecek veya daha önceden karşılaşılmayan tipten saldırılara karşı zafiyetler içermektedir. Saldırı Tespit Sistemleri (STS) bu zafiyetleri ortadan kaldırmak için öncelikle tercih edilebilecek uygulamalardır. Son geliştirilen STSleri incelendiğinde dinamik bir güvenlik mekanizması geliştirmek adına özellikle Makine Öğrenmesi tabanlı sistemlere ağırlık verildiği görülmektedir. Bilgisayar donanımları ve paralel hesaplama teknolojilerinde ortaya çıkan gelişmeler ve Büyük Veri işleme teknolojilerinin, Makine Öğrenmesi tabanlı sistemlerle uyumlu kullanıldığı görülmektedir. Bu çalışmada yedi farklı makine öğrenimi algoritmaları kullanarak STSlerin geliştirilmesi amaçlanmıştır. Elde edilen sonuçlar başarım, eğitim süreleri ve çalıştırma süreleri açısından karşılaştırılarak farklı kriterlere göre uygun algoritmanın ortaya konmuştur. Bu karşılaştırma için genel kabul gören NSL-KDD veri setinden faydalanılmıştır. Başarım sonuçlarına bakınca Adaboost algoritmasının en yüksek doğruluk oranına ulaştığı görülmektedir. Ancak gerek eğitim süresi gerekse çalışma zamanı performansı göz önüne alınınca Karar Ağacı algoritmasının daha yüksek performans gösterdiği, doğruluk oranı değeri itibarı ile de Adaboost’a yakın değere sahip olduğu görülmektedir.Publication Yalın Yazılım Geliştirme Sistematik Eşleme Çalışması(2014) Çatal, Çağatay; BAYDOĞMUŞ, GÖZDE KARATAŞ; 110942; 108363Publication Deep learning based security management of information systems: A comparative study(2020-01) Çebi, Cem Berke; Bulut, Fatma Sena; Fırat, Hazal; ŞAHİNGÖZ, ÖZGÜR KORAY; BAYDOĞMUŞ, GÖZDE KARATAŞ; 214903In recent years, there is a growing trend of internetization which is a relatively new word for our global economy that aims to connect each market sectors (or even devices) by using the global network architecture as the Internet. Although this connectivity enables great opportunities in the marketplace, it results in many security vulnerabilities for admins of the computer networks. Firewalls and Antivirus systems are preferred as the first line of a defense mechanism; they are not sufficient to protect the systems from all type of attacks. Intrusion Detection Systems (IDSs), which can train themselves and improve their knowledge base, can be used as an extra line of the defense mechanism of the network. Due to its dynamic structure, IDSs are one of the most preferred solution models to protect the networks against attacks. Traditionally, standard machine learning methods are preferred for training the system. However, in recent years, there is a growing trend to transfer these standard machine learning-based systems to the deep learning models. Therefore, in this paper, IDSs with four different deep learning models are proposed, and their performance is compared. The experimental results showed that proposed models result in very high and acceptable accuracy rates with KDD Cup 99 Dataset.Publication Genetic algorithm for intrusion detection system(IEEE, 345 E 47th St, New York, Ny 10017 USA, 2016) BAYDOĞMUŞ, GÖZDE KARATAŞ; 110942Intrusion detection systems are systems that prevent or slow the serving of different types of server data traffic caused by intensive use of networks that spread by internet. Particularly in recent years, because of increase in data density, need for these systems is increased thus different detection algorithms are being developed. In this study, information about the different detection algorithms using genetic algorithm, which are made of IDS algorithms is given and literature search's been made.