Person: ŞAHİNGÖZ, ÖZGÜR KORAY
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ŞAHİNGÖZ
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ÖZGÜR KORAY
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Publication Open Access Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini(Bitlis Eren Üniversitesi Rektörlüğü, 2020) Şişmanoğlu, Gözde; Koçer, Furkan; Önde, Mehmet ali; ŞAHİNGÖZ, ÖZGÜR KORAYSon yıllarda, bilgisayarların donanımındaki teknolojik gelişmeler ve makine öğrenme tekniklerindeki gelişmeler nedeniyle, "Büyük Veri" ve "Paralel İşleme" kullanımı olmak üzere problem çözmek için iki artan yaklaşım vardır. Özellikle GPU'lar gibi çok çekirdekli bilgi işlem aygıtlarında paralel olarak gerçekleştirilebilen Derin Öğrenme algoritmalarının ortaya çıkmasıyla, bu yaklaşımlarla birçok gerçek dünya problemleri çözülebilmektedir. Derin öğrenme modelleri eğitildikleri veri ile sınıflandırma, regresyon analizi ve zaman serilerinde tahmin gibi uygulamalarda büyük başarılar göstermektedir. Bu modellerin finansal piyasadaki en aktif uygulama alanlarından biri özellikle borsada işlem gören hisse senetlerinin tahmini işlemleridir. Bu alanda amaç, pazardaki değişim süreci hakkındaki hisse senedinin önceki günlük verilerine bakarak kısa veya uzun vadeli gelecekteki değerini tahmin etmeye çalışmaktır. Bu çalışmada, LSTM, GRU ve BLSTM isimli 3 farklı derin öğrenme modeli kullanılarak bir hisse senedi tahmin sistemi geliştirilip, kullanılan modeller arasında karşılaştırmalı bir analiz yapıldı. Spekülatif hareketlerden uzak olması için veri seti olarak 1968'den 2018'e kadar olan New York Borsası'ndan hisse senedinin zaman serisi değerlerini kullanıldı. Spesifik olarakta IBM hisse senedi ile test çalışmaları yapıldı. Deneysel sonuçlar BLSTM modelinin 5 günlük girdi verileriyle eğitilmesi ile %63,54 lük bir yönsel doğruluk değerine ulaşıldığını göstermektedir.Publication Restricted Detection of Phishing Websites by Using Machine Learning-Based URL Analysis(Institute of Electrical and Electronics Engineers Inc., 2020) Korkmaz, Mehmet; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, BanuIn recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the 'zero-day' attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyze the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate.Publication Restricted Intelligent Ambulance Management System in Smart Cities(Institute of Electrical and Electronics Engineers Inc., 2020) Akça, Tugay; ŞAHİNGÖZ, ÖZGÜR KORAY; Koçyiğit, Emre; Tozal, MücahidAccording to the United Nations' expectation, the total population of the cities will be doubled in the next three decades. This accelerating growth causes crucial problems in the main components of both traditional cities and smart cities. To increase the living quality of the residence in smart cities, enabling a clean, healthy, and sustainable environment are the major fields for the smart cities' managers and directors. One of the main infrastructures of the smart city is identified as smart health, which can be enabled with the use of modern technologies such as Internet of Things, especially for accessing the patients when they need help. In this Project, a smart ambulance management system is proposed in a smart city environment. If a patient needs an ambulance, the operator finds the nearest ambulance and direct it to the patient. The coordinates of ambulances are dynamically traced by the system, and Google Maps, as a third-party service, is used in order to calculate the shortest path to the casualty. After reaching to the patient, the expert (doctor or nurse) investigates the situation and finds the best available hospital by the proposed system. The experimental results showed that the proposed system finds the best solution in an acceptable ×.Publication Restricted Genetic Algorithm Based Optimized Waste Collection in Smart Cities(Institute of Electrical and Electronics Engineers Inc., 2020) Özmen, Mehmet; Şahin, Hasan; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years, the concept of smarts cities emerged to cope with the growth that cities around the world are facing. There are lots of problem areas in smart cities such as smart education, health, buildings, shopping, traffic management, etc. Waste management is one complex and effective problems of urbanization that is needed to be solved in smart cities. Route planning for waste collection and garbage trucks is a known issue in waste management. In this project, a genetic algorithm is proposed to address the problem of waste collection route using a truck fleet. The algorithm was tested in a simplified real state in single area and proved to be applicable to real-world scenarios based solely on the actual data of waste collection of cities.Publication Open Access Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset(IEEE, 2020) BAYDOĞMUŞ, GÖZDE KARATAŞ; Demir, Önder; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years, due to the extensive use of the Internet, the number of networked computers has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude on computers by using not only known but also new attack-types, which are more sophisticated and harder to detect. To protect the computers from them, Intrusion Detection System (IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don & x2019;t contain up-to-date data. Additionally, they are imbalanced and cannot hold sufficient data for all types of attacks. These imbalanced and outdated datasets decrease the efficiency of current IDSs, especially for rarely encountered attack types. In this paper, we propose six machine-learning-based IDSs by using K Nearest Neighbor, Random Forest, Gradient Boosting, Adaboost, Decision Tree, and Linear Discriminant Analysis algorithms. To implement a more realistic IDS, an up-to-date security dataset, CSE-CIC-IDS2018, is used instead of older and mostly worked datasets. The selected dataset is also imbalanced. Therefore, to increase the efficiency of the system depending on attack types and to decrease missed intrusions and false alarms, the imbalance ratio is reduced by using a synthetic data generation model called Synthetic Minority Oversampling TEchnique (SMOTE). Data generation is performed for minor classes, and their numbers are increased to the average data size via this technique. Experimental results demonstrated that the proposed approach considerably increases the detection rate for rarely encountered intrusions.Publication Metadata only Deep learning based security management of information systems: A comparative study(2020-01) Çebi, Cem Berke; Bulut, Fatma Sena; Fırat, Hazal; ŞAHİNGÖZ, ÖZGÜR KORAY; BAYDOĞMUŞ, GÖZDE KARATAŞ; 214903In recent years, there is a growing trend of internetization which is a relatively new word for our global economy that aims to connect each market sectors (or even devices) by using the global network architecture as the Internet. Although this connectivity enables great opportunities in the marketplace, it results in many security vulnerabilities for admins of the computer networks. Firewalls and Antivirus systems are preferred as the first line of a defense mechanism; they are not sufficient to protect the systems from all type of attacks. Intrusion Detection Systems (IDSs), which can train themselves and improve their knowledge base, can be used as an extra line of the defense mechanism of the network. Due to its dynamic structure, IDSs are one of the most preferred solution models to protect the networks against attacks. Traditionally, standard machine learning methods are preferred for training the system. However, in recent years, there is a growing trend to transfer these standard machine learning-based systems to the deep learning models. Therefore, in this paper, IDSs with four different deep learning models are proposed, and their performance is compared. The experimental results showed that proposed models result in very high and acceptable accuracy rates with KDD Cup 99 Dataset.Publication Restricted The Art of Machine Learning as Fashion Stylish for Designing Clothes(Institute of Electrical and Electronics Engineers Inc., 2022) KEYDAL, DUYGU; OYMAK, ERENCAN; DEMİR, KADİR BATUHAN; Yılmaz, Güray; ŞAHİNGÖZ, ÖZGÜR KORAYOver the years, designers have come to the fore with their originality and personal styles and have shaped the fashion industry with their designs. However, due to the progress of time, designers have become unable to meet the demands of all consumers. Since it takes a lot of time to produce an original design, the production process progresses slowly, and customers are uncomfortable with this situation. As in many other industries, designers are trying to solve this problem with the help of artificial intelligence, which is indispensable in the fields of commerce, art, and security. It first entered the fashion sector with drawing programs in the 1950s and has started to change the fashion sector since the 2000s. In the 1950s, artificial intelligence was used only to create a virtual drawing environment When the designer makes a mistake, he can simply erase the mistake and continue working on the design without having to start the whole design from scratch. These programs have greatly facilitated the work of designers. Designers are now able to draw their designs in a much shorter time. But even this shortened period is not enough for the whole fashion industry. Designers could still not keep up with the demands of all customers. Thanks to researchers who added different perspectives to artificial intelligence in the early 2000s with its usage not only for drawing but also for designing, Therefore, in this paper, it is aimed at producing some original designs by preserving the designer's style with the use of Aí techniques. With the proposed model, it has become able to produce ready-made designs by using features such as object detection and visual processing. The experimental results showed that Aí techniques are very successful for combining different patterns for producing an original fashion style. © 2022 IEEE.Publication Restricted Malware Detection in Android Systems with Traditional Machine Learning Models: A Survey(IEEE, 2020) Bayazit, Esra Çalık; ŞAHİNGÖZ, ÖZGÜR KORAY; Doğan, BuketDue to the increased number of mobile devices, they are integrated in every dimension of our daily life. To execute some sophisticated programs, a capable operating must be set up on them. Undoubtedly, Android is the most popular mobile operating system in the world. IT is extensively used both in smartphones and tablets with an open source manner which is distributed with Apache License. Therefore, many mobile application developers focused on these devices and implement their products. In recent years, the popularity of Android devices makes it a desirable target for malicious attackers. Especially sophisticated attackers focused on the implementation of Android malware which can acquire and/or utilize some personal and sensitive data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. In this work, we aimed to analyze the algorithms which are used in malware detection and making a comparative analysis of the literature. With this study, it is intended to produce a comprehensive survey resource for the researchers, which aim to work on malware detection.Publication Open Access An Evolutionary Approach to Multiple Traveling Salesman Problem for Efficient Distribution of Pharmaceutical Products(Institute of Electrical and Electronics Engineers Inc., 2020) Koçyiğit, Emre; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, BanuConsiderable growth of computer science has created novel solutions for variable problem fields and has increased the efficiency of available solutions. Evolutionary algorithms are quite successful in dealing with real-world problems that require optimization. In this article, we implemented a Genetic Algorithm that is well known evolutionary algorithm in order to provide an efficient solution for the Distribution of Pharmaceutical Products, which is a vital optimization problem, especially in situations such as a pandemic. The Multiple Traveling Salesman Problem approach was used to distribute pharmaceutical products as soon as possible. Moreover, we strengthened our proposal algorithm with 2-Opt Algorithm to get optimal results in earlier iterations. Different datasets from a library were applied to measure the quality of solutions and computation time. At the end of the work, we observed that our proposed algorithm generates successful solutions in an acceptable running time. This study will be extended with a new mutation concept as future work.Publication Restricted Feature Selections for the Classification of Webpages to Detect Phishing Attacks: A Survey(IEEE, 2020) Korkmaz, Mehmet; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, BanuIn recent years, due to the increased number of Internet-connected devices, almost all the real-world interactions are transferred to the cyberworld. Therefore, most of the commerce (especially in the e-commerce format) are executed over webpages. The anonymous and uncontrollable structure of Internet, enables the malicious use of this cyber environment for a relatively new crime format, named as e-crime, which mainly aims some illegal financial gain by cheating the standard end-users. Phishing attacks are one of the most preferred fraudulent technique which is used for getting some confidential information (like user-id, password, credit card information, etc.) of the end-users. Therefore, security admins of the networks try to decrease the number of victims is their companies. One principal protection mechanism is the use of blacklists to detect the phishing webpages. However, it has a significant deficiency in not protection about new page attacks. Most of the security admins use some learning systems which are trained by a pre-collected a-dataset by extracting some features from the URL and content of the web pages. The performance of the used system directly related with the features used for the classification. In this work, we aimed to analyze the previously used features in the classification of the web pages by making a comparative analysis about the literature. With this study, it is aimed to produce a general survey resource for the researchers, which aim to work on the classification of webpages or the security of the networks.