Graduate School of Informatics

YÖK 100/2000 Doktora Burs Programı 'nın aşağıda belirtilen öncelikli alan için Enformatik Enstitüsüne 04 Eylül 2019 Çarşamba günü mesai bitimine (17:00) kadar başvuru yapılacaktır.

Öncelikli alanlar:

● Veri Bilimi/Bulut Bilişim

Başvurular elden basılı dokümanlarla yapılacak olup aşağıda listelenen belgeler Enformatik Enstitüsü Öğrenci İşleri Ofisine teslim edilecektir.

Genel kosullar:

a) T.C. vatandaşı olmak.

b) Doktora programı için yüksek lisans derecesine, Bütünleşik doktora programı için

lisans derecesine sahip olmak.

c) Lisansüstü Eğitim ve Öğretim Yönetmeliğinin ve başvuru yapılacak yükseköğretim

kurumunun belirlediği başvuru ve kabul şartlarını taşıyor olmak.

ç) Kamu kurum ve kuruluşlarının kadro ve pozisyonları ile Devlet veya Vakıf

Yükseköğretim Kurumlarının öğretim elemanı kadrolarında çalışmıyor olmak.

d) Halen devlet yükseköğretim kurumunda doktora eğitimine devam ediyorsa tez

aşamasına geçmemiş olmak.

Gerekli belgeler:

● Transkript (Lisans ve Yüksek Lisans)

● ALES Sonuç Belgesi

● Yabancı Dil Sonucu

● Burs programına başvurmak istediğine dair başvurmak istediği alanı belirten ve iletişim bilgilerini içeren (adres, telefon numarası, e-posta) dilekçe

● Çalışma belgesi (İlgili Makama hitaben yazılacak olup işe başlama tarihi belirtilmiş olmalıdır.)

Halen ODTÜ'de doktora öğrencisi olmayan bursiyer adaylarının başvuru yapacakları öncelikli alanla uygun bir tez konusunda çalışmak üzere ODTÜ'de doktora başvurusu da yapmaları ve kabul edilmeleri gereklidir. Öncelikli alanla ilgili bir bölüm ve tez konusunda doktora öğrencisi olmayan adayların burs başvuruları kabul edilmeyecektir.

Kamu kurum ve kuruluşlarının kadro ve pozisyonları ile devlet veya vakıf yükseköğretim kurumlarının öğretim elemanı kadrolarında çalışanlar ile halen devlet yükseköğretim kurumunda doktora eğitimine devam eden öğrenciler bu burstan faydalanamayacaktır. Tez aşamasına geçmiş olmak için doktora tez önerisi aşamasını başarı ile tamamlamak gerekmektedir.

Related to the 2019-2020 Fall applications for Cyber Security (MS-Non Thesis) programs:

Interviews for MS with Non Thesis in Cyber Security will be held on September 05, 2019 at 09:30 a.m..

Note: Candidates who attended to interview on June 27 don’t have obligation to attend the interview again.

Candidates listed below are expected to be present at Classroom II-02.

Msc. Candidate: Fatma Mutlu

Program: Information Systems

Date: 03.09.2019 14:00 p.m

Place: A-108

Abstract: Talented employees is one of the most important factors that carries companies to success in today's business world. However, making employees work in the right position so that it is compatible with their ability, nature and capacity is a much more important factor for success. Ignoring this situation poses an obstacle to work in an efficient and effective way for the companies. The objective of this thesis is to provide a methodology to match employees with the right position by considering both technical and behavioral competencies. In this study, both the needs of the employees and requirements of positions are taken into considerations and weights of them are calculated. Then a multi objective optimization model is developed to make both employee and position satisfactory degree the most. Results of this study are used for the purpose of achieving high job satisfaction and productivity by improving bilateral matching evaluation of both workers and positions. This study also may be used as guidance in the planning of businesses related training and development activities. To show the applicability and contribution of the methodology developed, it is validated using real life data.

PhD. Candidate: Gürol Canbek

Program: Information Systems

Date: 02.09.2019 13:00 p.m

Place: Conference Hall 01

Abstract: This thesis proposes novel methods to analyze and benchmark binary-classification performance evaluation instruments. It addresses critical problems found in the literature, clarifies terminology and distinguishes instruments as measure, metric, and as a new category indicator for the first time. The multi-perspective analysis introduces novel concepts such as canonical form, geometry, duality, complementation, dependency, and leveling with formal definitions as well as two new basic instruments. An indicator named Accuracy Barrier is also proposed and tested in re-evaluating performances of surveyed machine-learning classifications. An exploratory table is designed to represent all the concepts for over 50 instruments. The table’s real use cases such as domain-specific metrics reporting are demonstrated. Furthermore, this thesis proposes a systematic benchmarking method comprising 3 stages to assess metrics’ robustness over new concepts such as meta-metrics (metrics about metrics) and metric-space. Benchmarking 13 metrics reveals critical issues especially in accuracy, F1, and normalized mutual information conventional metrics and identifies Matthews Correlation Coefficient as the most robust metric. The benchmarking method is evaluated with the literature. Additionally, this thesis formally demonstrates publication and confirmation biases due to reporting non-robust metrics. Finally, this thesis gives recommendations on precise and concise performance evaluation, comparison, and reporting. The developed software library, analysis/benchmarking platform, visualization and calculator/dashboard tools, and datasets were also released online. This research is expected to re-establish and facilitate classification performance evaluation domain as well as contribute towards responsible open research in performance evaluation to use the most robust and objective instruments.

Msc. Candidate: Mustafa Mert Karataş

Program: Cyber Security

Date: 09.09.2019 13:00 p.m

Place: A-212

Abstract: The human body is exposed to several pathogens during its lifetime. HIS(Human Immune System) is responsible to protect the body from different pathogens. HIS has two distinct response systems to these outsiders, which are; innate and adaptive immune systems. While the innate system takes general actions to the intruding pathogens, the adaptive immune system eradicates them by its special cells. T-Cells, one of the defined adaptive immunity cells, are created in the thymus. The generation of these cells is constant and continued to the end of the human life span. T-Cells protects the human body with the use of its distinct self and non-self discrimination ability. In the computer science domain, self/non-self discrimination of the T-Cells are studied and applied in the subject of AIS (Artificial Immune System). A model observed from the HIS while creating these cells, Negative Selection, is added as an algorithm to this subject. The ability to discriminate self from non-self is thought to be useful for the detection of any malicious activity in a computer or a computer network. In this thesis, the Negative Selection Algorithm of the T-Cells is applied in order to detect malicious user input that is submitted from HTTP GET parameters. Detection is done through detectors strings with varying lengths. Detectors are constructed with randomly chosen n-gram strings generated from the training dataset. The number of n-gram strings to form a single detector is determined with the use of Poisson Probability. Detection rates, number of attempts needed for generating a single detector, average detection rates for each detector, the lengths of the detectors and number of detectors that can be generated over a course of time are calculated and presented.

Msc. Candidate: Muhsin Aldemir

Program: Information Systems

Date: 02.09.2019 15:30 p.m

Place: A-108

Abstract: Malicious websites pose major risks to users and businesses including economic damages, privacy breaches and loss of valuable data. Malicious actors use websites as a spreading medium for their motives. Analyzing the relationships between malicious websites and comparing them to benign ones can help understand the problem better, and enable detection and prevention of these websites more accurately. This thesis focuses on detection and characterization of malicious websites using Social Network Analysis (SNA). SNA provides powerful methodologies for discovering and visualizing the relationships between actors. By utilizing the links in between and among malicious and benign websites, graphs were constituted, whose nodes were websites and ties were hyperlinks between them. For this purpose, the data which included the snapshot of the pairwise links amongst millions of websites, the list of malicious websites and their types were obtained from the web. First, the same size networks of malicious and benign websites were characterised and compared using their descriptive properties. Then, using these networks new analyses were carried out to determine malicious websites and their types based on their network structures and link similarities. Results were presented showing the detection accuracies of applied methods.

Related to the applications made to the Department of Modelling and Simulation Multimedia Informatics PhD and MS Programs for 2019-2020 Acadeic Year Fall Term, 2^nd group interviews will be held on Tuesday, September 3, 2019.

Candidates listed below are expected to be present in front of Classroom II-01 (S-01) 15 minutes before of their scheduled time of interview.