Verinin boyut ve çeşitlilik olarak arttığı, kişisel verilerin kolaylıkla paylaşıldığı ve ihlallerinin sayısının hızla yükseldiği günümüzde veri mahremiyeti, üzerinde çokça çalışılan ve önlemler geliştirilen konuların başında gelmektedir. Kişisel verileri kullanan, depolayan veya işleyen her türlü uygulama, ürün veya sistem, veri mahremiyetini sağlamak, korumak ve doğru bir şekilde uygulandığını göstermek zorundadır. Son yıllarda veri mahremiyeti kapsamında pek çok yeni çözümler geliştirilse de teknolojik gelişmeler, yapay zekâdaki ilerlemeler, derin öğrenme yaklaşımlarının uygulama başarısı, bu yaklaşımların pek çok alanda kullanılmaya başlanması ve yapısı itibariyle kara-kutu çözüm sağlaması, veri mahremiyeti açısından yeni endişeleri de beraberinde getirmiştir. Bu çalışmada, günümüzün önemli yapay zekâ teknolojilerinden biri olan derin öğrenmede, kişisel bilgi içeren verilerin analiz edilmesi sürecinde mahremiyet koruyucu çeşitli önlemler incelenmiş, bu önlemlerden en çok kullanılanı olan diferansiyel mahremiyet açıklanmış ve derin öğrenmedeki uygulamaları karşılaştırılmıştır. Çalışmamızın, kişisel verileri işleyen derin öğrenme tabanlı uygulamalarda, oluşabilecek ihlallerin önlenmesine, karşılaşılabilecek risklerin doğru belirlenmesine ve gereken önlemlerin daha sağlıklı alınmasına katkı sağlayacağı değerlendirilmektedir.

Developing a new privacy preserving model  Utility-based model  Outlier record-oriented model Figure A. General representation of the proposed model Purpose: In this paper, a new outlier record-oriented utility-based privacy preserving model was proposed. The existence of outliers in data set decreases data utility in anonymization. Hence, outliers should be managed in the anonymization process. In traditional management approaches, outliers are detected after anonymization and they are partially or completely removed from the published data set. However, detection of outliers after anonymization increases computational cost and the removal of the outliers from the data set reduces total data utility. In this study, a new outlier-oriented utility-based privacy preserving model named as OAN, which reduces the computational cost by detecting outliers before anonymization and increases data utility by using all data, was proposed. Theory and Methods: In the proposed model, data is divided into two subsets which are named as utility record set (URS) and outlier record set (ORS). URS includes normal data that presents high data utility. ORS is the set of outlier records that decreases total data utility. In order to increase total data utility, ORS is divided into two new subsets such as URS and ORS sets recursively. If a stopping criteria is met, this recursion stops and finally anonymized data is released. Local Outlier Factor was employed for outlier detection and anonymization was performed by using Mondrian algorithm. DM and AECS metrics were used to evaluate the information loss of the proposed model. Results: In the experiments, Adult data set was used to test the proposed model. DM and AECS metrics were employed to measure data utility. It was observed that the results of the proposed model presented better results than classical Mondrian. The results showed that OAN increases total data utility while preserving data privacy. In addition, it was showed that the proposed model is computational cost-effective compared to another utility based anonymization model. Conclusion: In this paper, a new outlier record-oriented utility-based privacy preserving model was proposed, tested and verified. Two information metrics and Adult data set were employed in the experiments and the results showed that the proposed model is an effective solution in terms of computational cost and data utility.

 Mahremiyet koruma modelleri (Privacy preserving models)  Büyük veri yayınlama modelleri (Big data publishing models)  Kavramsal model önerileri (Conceptual model suggestions)  Mahremiyet korumalı büyük veri yayınlamada ilk kavramsal model önerileri (First conceptual model suggestions for privacy preserving big data publishing)

 Driver behavior analysis based on big data  New models for fleet management  Real data driven models Figure A. Proposed big data based driver behavior analysis model for fleet managementPurpose: In this study, the deficiencies of fleet management systems have been examined from both big data and driver behavior perspectives, and the driver/driving behavior has been analyzed on real data and as a result, big data based new models have been proposed to perform data analysis. With 6 different scenarios on big data, new models have been developed with big data-based approaches that determine the differences in behavior of the drivers in the fleet, the behavior at various locations and the behavior at specific points. Theory and Methods:In this study, the deficiencies of fleet management systems have been examined both in terms of big data and driver/driving behavior, real scenarios has been determined and map reduce based models have been proposed to solve this problem with the real big fleet data for the first time. Results:From the obtained results, it was determined that (1) in particular, among drivers exceeding the speed limit of more than 50%, certain drivers have a 30% share of these violations compared to other drivers. (2) Even if the average speed is the same, there can be 6 times the difference in speed violations number between drivers, similar to that, even if the number of speed violations are the same, there could be a 2-fold difference in violation times. (3) According to the seasonal analysis, the highest number of speed violations occur in the summer season. However, speed violation duration occurred in autumn at most.(4) Roads where speed limit is exceeded in Ankara are Yenimahalle with a rate of 23.6% on a district basis, Saray with a rate of 4.62% on a quarter basis, Eskişehir road with a rate of 6.85% on intercity roads basis , and Anadolu Boulevard with 2.74% on urban roads basis. Finally, (5) it has been found that differences of near 300% occur in the analysis of 3 different radar points according to the number of speed violations before and after 1 kilometer of radar points. Conclusion:As a result, by using big data analytics, fleets can be used more easily and manageable within the scope of driver/driving behavior. These models can be used to prevent cost and work loss, and these analyzes for Ankara province can be used for other provinces. It is also considered that different values can be produced such as the analysis made at speed radar points.

ÖZETBu çalışmada, mevcut sunucu tabanlı POP3 uygulamalarına IPv6 desteği verilmesi için gerekli olan altyapı yazılım desteğinin neler olması ve konu ile ilgili yapılması zorunlu değişikliklerin neler olması gerektiği ve bunun için örnek bir uygulamanın nasıl geliştirilebileceği adım adım ilk kez anlatılmıştır. İlk kez önerilen çalışmada sonuç olarak; gelen e-postaların IPv6 üzerinden iletilmesini sağlamak amacıyla IPv6'nın nasıl yapılandırılması gerektiği bu makalede anlatılmış, geliştirilen uygulama Microsoft Office Outlook programı kullanılarak test edilmiş ve IPv6 destekli soket tabanlı yazılımların geliştirilmesi noktasında ilk kez çözüm önerileri sunulmuştur. Anahtar kelimeler: IPv6, IPv6 Yapılandırma, IPv6 Destekli Yazılım, Soket Tabanlı Yazılım A SOFTWARE SUGGESTION FOR IPv6 SUPPORTED SOCKET BASED SERVERS ABSTRACT ABSTRACTIn this study, a sample application has been explained step by step the first time to provide IPv6 support necessary infrastructure software support, what changes should be done for an existing server based on POP3 application. IPv6 configuration has been carried out in order to ensure received e-mails transmission over IPv6 with the developed software. The software has been also tested using Microsoft Office Outlook and the solution has been presented to the literature for the first time in the point of development of IPv6 supported socket-based software.

Bu çalışmada, yörünge verilerinin yayınlanmasında diferansiyel mahremiyeti kullanan yeni bir model önerilmiş, başarıyla geliştirilmiş ve gerçek bir veri kümesi üzerinde test edilmiştir. / In this paper, a new trajectory data publishing model using differential privacy was proposed, developed and tested on a real dataset. Şekil. Önerilen modelin akış şeması /Figure. General flowchart of the proposed model Amaç (Aim)Mahremiyet korumalı yörünge verisi yayınlamak için yeni bir model geliştirilmesi amaçlanmıştır. / It was aimed to develop a new model for privacy preserving trajectory data publishing.

Siber terörizm eylemlerinde etkili bir araç olarak kullanılan DDoS saldırıları 1980'li yıllarda amatör bilgisayar korsanları (script kiddies) tarafından oyun/gösteriş amaçlı gerçekleştirilmeye başlamıştır. Bu saldırılar ile ciddi ekonomik zararlar verebileceğini fark eden siber suçlular, 90'lı yıllarda DDoS saldırılarını elektronik ticaret şirketlerinden şantaj ile para kazanma aracı olarak kullanmaya başlamışlardır. 2000'li yıllarda ise DDoS siber protesto ve saldırı aracı olarak kullanılmaktadır. Bu çalışmada DDoS saldırılarının gerçekleştirilme nedenleri tarihsel değişimi ile incelenmektedir. Ayrıca; DDoS saldırılarını gerçekleştirmekte kullanılan yöntemler, kategorize edilip örnekler ile anlatılmaktadır. DDoS 'un bir siber terör aracı olarak nasıl kullanıldığı açıklanmaktadır. Ayrıca, siber terörizm ile DDoS saldırıları arasındaki ilişki sunulmaktadır.

Objective: Artifi cial Neural Networks (ANNs) trained with backpropagation learning algorithm have been used commonly in previous studies. This study presents radial basis function neural network (RBFNN), a special kind of neural network, and logistic regression analysis (LRA) for prognostic classifi cation of Coronary Artery Disease (CAD). Methods:The records of 237 consecutive people who had been referred for the department of Cardiology were used in the analysis. Radial basis function neural network and logistic regression analysis were used for CAD classifi cation. Results:The results have shown that LRA and RBFNN were both successful for classifi cation and might be used for non-invasively based on clinical variables in the classifi cation of diseases like CAD. Conclusions:The work can be concluded that LRA performed the classifi cation better than RBFNN for prognostic CAD classifi cation in the present CAD data. However, RBFNN, utilizing larger sample sizes, can have better classifi cation accuracy. For more defi nite comparison, simulation studies should be carried out using various methods.