Graduate School of Informatics

Msc. Candidate: Babak Allahgholipour

Program: Multimedia Informatics

Date: 09.12.2019

Place: A-212

Abstract: Information about multisensory perception and brain cognition mechanism lead to a better virtual world creation. Brain store visual and auditory information to use in predicting future events. Image memory takes information from visual clues which during speech recognition happens by recording visual information and its auditory mappings. Based on this process, brain create prediction mechanism for future events. In speech recognition brain record sound and its visual presentation. When brain encounter difficulty in perception an auditory stimuli, it looks for visual mappings to guess the result. Considering the fact that some completely unrelated similarity may exist, some mapping may not lead to better understanding the world around. When previous information mislead the brain to understand the situation cognitive biases happen. McGurk Effect discusses how different lip movements affect auditory perception to get disparate information. Variety of factors influence amount of dependency on visual clues. Tracking speaking, especially in challenging environments, can be increased by visual clues such as face expressions, tongue and lips movements. Considering these elements in virtual world and game designing lead to improving believability. Perception processes, brain nature, designing factors, graphical elements, and visual structures are discussed to find out improved ways in designing realistic auditory and visual components.

02.12.2019 tarihinde yapılan giriş sınavı sonucunda Kanser Sistem Biyolojisi Laboratuvarı (KanSİL) Uygulamalı Birim Öğretim Görevlisi olmaya hak kazanan adayın ismi aşağıda belirtilmektedir.

For further information please visit Graduate Awards.

Application deadline: 3 January 2020

Tez ödülleri hakkında detaylı bilgi için Lisansüstü Ödülleri sayfasını ziyaret edin.

Son Başvuru Tarihi: 15 Aralık 2020

Assoc. Prof. Nurcan Tuncbag from METU Health Informatics was awarded with Aziz Sancar Science and Incentive Awards, given by TÜSEB(Turkey Institutes of Health Administration).

Msc. Candidate: Melih Turhanlar

Program: Cybersecurity

Date:  06.11.2019 13:30

Place: A-108

Abstract: Phishing Attacks are a type of social engineering attack which aims to steal victim’s credit card numbers, credentials, and personal information by using victim’s curiosity and fear emotions. On these attacks, attackers send link with a text which triggers the victim's curiosity and fear emotions. If victim panic with the text inside attack vector and click the link, then victim connects to so-called web server or site which looks like a real web site but actually under control of the attacker. By filling the HTML forms inside the so-called web site, victims send their credentials unwittingly not to the real web site but actually to the attacker. In this study, we focused on phishing attacks with Turkish text in it. The data that we have used in that study contains not only Turkish phishing emails, but also Turkish phishing SMS and tweets that we gathered from the internet during the study. Focusing on Turkish texts inside the attacker’s attack vector, with Logistic Regression Synthetic Minority Over-Sampling Technique (0.923) F1 score has been achieved on our imbalanced dataset which we have created that can be used in other academic researches.

PhD Candidate: SALVA DANESHGADEH ÇAKMAKÇI 
Program: Information Systems
Date: 22.11.2019 10:00
Place: Conference Hall 1

Abstract: Distributed denial-of-service (DDoS) attacks are constantly evolving as the computer and networking technologies and attackers’ motivations are changing. In recent years, several supervised DDoS detection algorithms have been proposed. However, these algorithms require a priori knowledge of the classes and cannot automatically adapt to the frequently changing network traffic trends. This emphasizes the need for the development of new DDoS detection mechanisms that target zero-day and sophisticated DDoS attacks. To fulfill this need, an online sequential DDoS detection scheme that is suitable for use with multivariate data was proposed. The proposed algorithm utilizes a kernel-based learning algorithm, the Mahalanobis distance, and a chi-square test. The algorithm is fully automated and does not require a pre-defined setting of any thresholds or baseline normal network traffic for training. Initially, four entropy-based and four statistical features were extracted from network flows as detection metrics per minute. Then, the kernel-based learning algorithm was employed to detect entropy-based input feature vectors that were suspected to be DDoS. This algorithm assumes no model for network traffic or DDoS; then, it constructs and adapts a Dictionary of features that approximately span the subspace of normal behavior. Every T minutes, the Mahalanobis distance between suspicious vectors and the distribution of Dictionary members is measured. Subsequently, the chi-square test is used to evaluate the Mahalanobis distance. The proposed DDoS detection scheme was applied to the CICIDS2017 dataset and the performance of the algorithm was measured using different performance metrics including accuracy, recall, precision and ROC-Curve. Finally, the results were compared with those by existing algorithms. It was demonstrated that the proposed online detection scheme outperforms almost all available DDoS classification algorithms with an offline learning process.