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.

KANSİL Laboratories instructor exam will be held on 14 October 2019 Monday at 14:00 in A Block 108. The name of the candidates who will take the evaluation exam are as follows:

PhD Candidate: Ali Mert Ertuğrul
Program: Information Systems
Date: 20.11.2019 15:30
Place: Conference Hall 1

Abstract: The relationships between individual activities and societal events (e.g. migrations, social movements) are complex due to the various social, temporal and spatial factors. Understanding such relationships in the context of various societal events such as street protests and opioid crisis, and forecasting these events is important as they have great impacts on public policies and supporting decision making of authorities. In this thesis, novel, spatio-temporal, deep neural networks are proposed (i) to forecast societal events and (ii) to help examine the relationships between societal events and their social and geographical contexts. The proposed models are designed to model the complex interactions between local (observed from within a location) and global (observed from all locations) activities by incorporating a new design of attentional networks. They are capable of forecasting the occurrence of future societal events and allow for interpreting what features, from which places, and how they contribute to event forecasting. Within the scope of this thesis, extensive experiments are conducted to evaluate the proposed networks on two different types of population-level societal events, namely social movements and opioid overdoses, with multiple datasets. The results indicate that the proposed models achieve superior forecasting performance than the compared methods. Also, they provide meaningful interpretations in terms of (i) what local and global activity features are more predictive, (ii) what locations have more salient contributions, and (iii) how these locations contribute to forecasting the subsequent events.

Siber Güvenlik EABD Tezsiz Yüksek Lisans Programı 2.Grup Mülakat Sonuçları