Browsing by Author "Gezer, Cengiz"

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Credit Card Fraud Detection with Machine Learning Methods
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 01.01.2019) Goy, Gokhan; Gezer, Cengiz; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric.
Generating Emergency Evacuation Route Directions Based on Crowd Simulations with Reinforcement Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) Unal, Ahmet Emin; Gezer, Cengiz; Pak, Burcu Kuleli; Gungor, Vehbi Cagri; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri
In an emergency, it is vital to evacuate individuals from the dangerous environments. Emergency evacuation planning ensures that the evacuation is safe and optimal in terms of evacuation time for all of the people in evacuation. To this end, the computer-enabled evacuation simulation systems are used to generate optimal routes for the evacuees. In this paper, a dynamic emergency evacuation route generator has been proposed based on indoor plans of the building and the locations of the evacuees. To generate the optimal routes in real-time, a reinforcement learning algorithm (proximal policy optimization) is presented. Comparative performance results show that the proposed model is successful for evacuating the individuals from the building in different scenarios.
OFFER : Referees Suggester for the Journal Editors
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019) Coskun, Mustafa; Hacilar, Hilal; Gezer, Cengiz; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Assigning appropriate referees to a journal or conference paper is a vital task for many reasons, including enhancing the journal venue quality and reliance, fair judgement of the papers, and among many others. While assigning the referees to the papers, the editors of a journal venue need to find suitable referees who are both related to field of the given paper and have no conflict of interest with the authors of the paper. Editorial-wise this referee assignment process is implemented in a hand-crafted manner, i.e., the editor needs to find the most suitable referees to the paper via a search engine and manually refines the all unrelated and having conflict of interest authors to the paper authors. Clearly, such a manual referee searching process is tedious and time consuming for the editors. In this paper, we present an alternate automated approach for assigning referees problem using intrinsic random walk with restart proximity measure. In our experiments based on a comprehensive DBLP networks, we show that our approach, called OFFER, significantly outperforms state-of-the-art the random walk with restart based method.
A survey on information security threats and solutions for Machine to Machine (M2M) communications
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA, 2017) Tuna, Gurkan; Kogias, Dimitrios G.; Gungor, V. Cagri; Gezer, Cengiz; Taskin, Erhan; Ayday, Erman; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü;
Although Machine to Machine (M2M) networks allow the development of new promising applications, the restricted resources of machines and devices in the M2M networks bring several constraints including energy, bandwidth, storage, and computation. Such constraints pose several challenges in the design of M2M networks. Furthermore, some elements that contributed to the rise of M2M applications have caused several new security threats and risks, typically due to the advancements in technology, increasing computing power, declining hardware costs, and freely available software tools. Due to the restricted capabilities of M2M devices, most of the recent research efforts on M2M have focused on computing, resource management, sensing, congestion control and controlling technologies. However, there are few studies on security aspects and there is a need to introduce the threats existing in M2M systems and corresponding solutions. Accordingly, in this paper, after presenting an overview of potential M2M applications, we present a survey of security threats against M2M networks and solutions to prevent or reduce their impact. Then, we investigate security-related challenges and open research issues in M2M networks to provide an insight for future research opportunities. Moreover, we discuss the oneM2M standard, one of the prominent standard initiatives for more secure and smoother M2M networks and the Internet of Things. (C) 2017 Elsevier Inc. All rights reserved.