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Öğe A deep learning-based solution for digitization of invoice images with automatic invoice generation and labelling(Springer Heidelberg, 2024) Arslan, Halil; Isik, Yunus Emre; Gormez, YasinNowadays, the level of invoice traffic between companies has reached enormous levels. Invoices are crucial financial documents for companies, and they need to extract this information from these documents to access and control them quickly when necessary. While electronic invoices can be easily transferred to the company's ERP system with the help of integrators, information from printed invoices must be entered into the ERP system. Information entry is generally performed manually by company employees, so the probability of error is high. The automatic recognition of information in printed invoices will reduce the possibility of error. It will also save time and money by reducing workforce requirements. This study proposes a deep learning-based solution for detecting fields in image invoices that are in high demand among businesses. The system offers an end-to-end solution, which includes a novel method for generating synthetic invoices and automatic labeling. Three invoice templates were used to evaluate the usability of the system and an adaptive fine-tuning-based solution is proposed for newly coming invoice templates. Furthermore, 6 different object detection models were compared to find the most suitable one for our problem. The system was also tested with 1022 real invoice images that were manually labeled to test real-world usage. The results indicated that the fine-tuned model achieved an accuracy that was 8.4% higher than the baseline models. In tests performed on CPU, TOOD and Cascade-RCNN models were the most successful algorithms, while YOLOv5 was the fastest running algorithm. Depending on the priority of the needs, both algorithms can be preferred for real-time usage in the detection of invoice fields. The synthetic invoice generation code is available at https://github.com/SCU-CENG/Invoice-Generation.Öğe A novel hybrid model for bluetooth low energy-based indoor localization using machine learning in the internet of things(Pamukkale Univ, 2024) Gormez, Yasin; Arslan, Halil; Isik, Yunus Emre; Tomac, SercanIndoor localization involves pinpointing the location of an object in an interior space and has several applications, including navigation, asset tracking, and shift management. However, this technology has not yet been perfected, and many methods, such as triangulation, Kalman filters, and machine learning models have been proposed to address indoor localization problems. Unfortunately, these methods still have a large degree of error that makes them ill-suited for difficult cases in real-time. In this study, we propose a hybrid model for Bluetooth low energy -based indoor localization. In this model, the triangulation method is combined with several machine learning methods (naive Bayes, k -nearest neighbor, logistic regression, support vector machines, and artificial neural networks) that are optimized and tested in three different environments. In the experiment, the proposed model performed similarly to the solo triangulation model in easy and medium cases; however, the proposed model obtained a much smaller degree of error for hard cases than either solo triangulation or machine learning models alone.Öğe A systematic literature review on ransomware detection by evidence-based software engineering method(Sivas Cumhuriyet Üniversitesi, 2024) Kuzu, Engin; Kekül, Hakan; Arslan, HalilRansomware attacks, which aim to take ransom by encrypting the files they infect with unbreakable passwords, have become an increasing threat in recent years. Decrypting encrypted files without data loss is nearly impossible without the encryption key. This often obliges ransomware victims to pay the amount of the ransom demanded. The purpose of our study is to present a systematic literature review of ransomware detection research. The method we base on while performing a systematic literature review is the Evidence-Based Software engineering approach. This approach is based on the Evidence-Based Medicine method, which has been successfully applied in many fields. Six steps of Evidence-Based Software Engineering have been implemented in sequence. For this purpose, 114 scientific articles, which fall within the scope of our research questions, were researched from the studies conducted between 2017 and 2022 on ransomware detection. According to our quality evaluation rules, 49 articles meeting our quality criteria were analyzed. The answers to our research questions, which we determined through the analyzed articles, are presented in detail.Öğe Blockchain and Security in the IoT Environments: Literature Review(IEEE, 2018) Arslan, Halil; Aslan, Hilal; Karki, H. Dogan; Yuksel, A. GurkanThe Internet of Things (LOT) has been the focus of research in recent years. It is inevitable that some security problems arise due to the increased need and demand for intelligent systems and that these problems frequently come up. Security and confidentiality are the most fundamental issues for IOT applications, and despite all the work done, there are still a number of major challenges and vulnerabilities. In order to facilitate the existing security vulnerabilities and challenges, the LOT has focused on the safety of the research process. In this study, current studies in the literature arc examined in LOT devices. As a result of the examination, some results were obtained, recommendations were made and compared with other methods.Öğe Blockchain Based Single Sign-On Support for IoT Environments(IEEE, 2019) Arslan, Halil; Aslan, HilalIoT technologies, According to the Gartner report for 2015, include billions of dollars in investment and a high academic research area. The issue of security in IoT systems comes to the fore and important studies are carried out on the problems encountered. In this study, a new blockchain approach to user access management problem for IOT environments is proposed in the light of current literature in the literature, considering the application-level security vulnerabilities in IOT devices. The single sign-on method discussed in access management is an important technique that helps to coordinate different authentication mechanisms, user logon, and user account management functions within an organization at both the point of availability and security. The blockchain offers an advanced security for systems with its encrypted and distributed data structure. In this context, blockchain technology for user access management in IoT devices and keycloak which provides a single sign-on infrastructure for authentication and session management on devices, are used.Öğe Blockchain Based User Management System(IEEE, 2020) Temiz, Mustafa; Soran, Ahmet; Arslan, Halil; Erel, HilalBlockchain is a reliable and transparent structure formed by distributing the data in blocks connected to each other using various cryptography techniques to other points on the network. The difference from the existing database operations is that the authorities and responsibilities do not exist in a single central authority, and that these powers and responsibilities are distributed to the other nodes in the network and the assignment is shared. To provide this, peer to peer network infrastructure is used. However, at this stage, authentication in terms of security is one of the basic security mechanisms. In this study, a user management system which can be integrated with more reliable and current technologies, which is thought to be the solution to speed problems in blockchain, is proposed.Öğe Classification of Customer Demands by Organizational Workflows(IEEE, 2018) Arslan, Halil; Kaynar, Oguz; Sahin, SumeyyeCorporate firms that aim to be permanent in the long term should not lose existing customers and need to win new customers. The increase in the number of firms offering similar services also increases the alternatives for customers. For this reason, there is no guarantee of long-term working with a customer. It has become compulsory to manage customer demands for companies that are in a highly competitive environment. In order to be able to sustain customer loyalty in the long term, they should better recognize them and provide quick returns to their demands. Firms are using help desk applications to manage these demands. Help desk applications are systems that aim to provide information and support to customers or end users about firms' services. In this study, customer demands were analyzed using text mining and machine learning algorithms and classified according to organizational workflow. The data sets used in the study and customer demands were obtained from help desk belonging to Detaysoft which offers live support to over 300 customers.Öğe Classification of Customer Demands by Using Doc2Vec Feaure Extraction Method(IEEE, 2019) Arslan, Halil; Kaynar, Oguz; Sahin, SumeyyeCompanies provide after-service communication with their customers through help desk systems they provide in call centers and websites. In companies with large networks, the amount of data collected by using these communication tools is increasing day by day.The separation of these collected requests and feedbacks to be transferred to the relevant units has become a very time consuming process. The prolongation of this process in customer-oriented companies can lead to customer loss.Therefore, it has importance to transfer, evaluate and return requests from such firms. In this study, firstly, two different models were used to obtain the features from the demands of customers. The features obtained were classified by multi-layer artificial neural network and it was provided the related demand was transferred to the related unit. Thus, the usability of the Doc2Vec model, which can be used as an alternative to the classic word bag, is examined in Turkish text classification studies.Öğe Comparison of Data Transfer Performance of BitTorrent Transmission Protocols(2019) Arslan, Halil; Canay, ÖzkanBitTorrent, one of the distributed file sharing protocols, is regarded as one of the first examples of decentralized Internet philosophy and is among the important research areas in this context. TCP was initially used as the transport layer protocol in BitTorrent, and the transition to the uTP protocol was made because of the problems of latency and excessive bandwidth consumption. Later, with WebTorrent, which is a BitTorrent protocol adapted to the web, WebRTC was proposed as a transport layer protocol. Thus, BitTorrent protocol is enabled to work directly through Internet browsers without using any plugin. In this study, the data exchange sizes in the torrent shares of these three transmission protocols have been compared and the advantages and disadvantages of these protocols were demonstrated in this context.Öğe Comparison of the Web Based Multimedia Protocols for NAT Traversal Performance(IEEE, 2015) Arslan, Halil; Tuncel, Sinan; Yuksek, A. GurkanRTMFP and WebRTC standards for Web-based real-time media data sharing applications are provided significant gains. These types of applications and are among the topics researched intensely. A model for the web based real time transmission of media data is suggested within this study. WebRTC and RTMFP standards are compared with regard to the factors affecting the service quality such as packets for signaling and connectability as to be between different NAT types, addressing specifically the techniques that are used for NAT transmission. Advantages and disadvantages are demonstrated.Öğe Design of Project Based Positioning and Project Planning System for Mobile Employee(IEEE, 2018) Arslan, Halil; Yuksek, A. Gurkan; Gun, OsmanIt is important that the employee of mobile-based companies (software consultancy, etc.) on the computer are able to make position takings and integrate these positions with project planning systems. This study targets an abstractly designed software product that can integrate with other enterprise applications where location identification and follow-up of personnel using IP based system (tablet, phone, pc, etc.) can be done. After this software product collects the physical location definitions of the employee (GPS, WPS and IP) and performs the appropriate validation steps, it provides outputs that can be done on a project / customer basis based on location / employee location by converting logical locations like project, customer etc.. It is anticipated that this study will provide significant gains and administrative facilities for mobile employee companies by designing to meet the need to know which employee is in which location.Öğe Developing Novel Deep Learning Models to Detect Insider Threats and Comparing the Models from Different Perspectives(2024) Görmez, Yasin; Arslan, Halil; Işık, Yunus Emre; Gündüz, VeyselCybersecurity has become an increasingly vital concern for numerous institutions, organizations, and governments. Many studies have been carried out to prevent external attacks, but there are not enough studies to detect insider malicious actions. Given the damage inflicted by attacks from internal threats on corporate reputations and financial situations, the absence of work in this field is considered a significant disadvantage. In this study, several deep learning models using fully connected layer, convolutional neural network and long short-term memory were developed for user and entity behavior analysis. The hyper-parameters of the models were optimized using Bayesian optimization techniques. Experiments analysis were performed using the version 4.2 of Computer Emergency and Response Team Dataset. Two types of features, which are personal information and numerical features, were extracted with respect to daily activities of users. Dataset was divided with respect to user or role and experiment results showed that user based models have better performance than the role based models. In addition to this, the models that developed using long short-term memory were more accurate than the others. Accuracy, detection rate, f1-score, false discovery rate and negative predictive value were used as metrics to compare model performance fairly with state-of-the-art models. According the results of these metrics, our model obtained better scores than the state-of-the-art models and the performance improvements were statistically significant according to the two-tailed Z test. The study is anticipated to significantly contribute to the literature, as the deep learning approaches developed within its scope have not been previously employed in internal threat detection. Moreover, these approaches have demonstrated superior performance compared to previous studies.Öğe Development of a central controlled automation project on the IoT platform(Peter Lang AG, 2019) Yüksek, Ahmet Gürkan; Arslan, Halil; Çifçi, Gülşah; Elyakan, M. Lemi[No abstract available]Öğe DtyPAM: Kurumsal Destek Firmaları için Önerilmiş Konteynır Tabanlı Ayrıcalıklı Erişim Yönetim Sistemi(Mersin Üniversitesi, 2023) Şimşek, Hamza Kürşat; Arslan, Halil; Görmez, YasinBilişim alanında önceki zamanlarda da uygulanan uzaktan destek ve uzaktan çalışma kavramları, 2019 yılında başlayan ve tüm dünyayı etkisi altına alan COVID-19 salgını ile hemen hemen tüm sektörler tarafından uygulanmaya başlamıştır. Ölçeği ne olursa olsun bütün girişimler dijital uygulamaları kullanmakta ya da kullanma planı yapmaktadır. Özellikle holding düzeyindeki firmalar, birçok iş sürecini karmaşık kurumsal kaynak planlama uygulamaları üzerinden yürütmektedir. Bu uygulamalar içinse genellikle dış kaynaklardan destek almakta ve bu destekler günümüzde sıklıkla uzaktan yapılmaktadır. Bu aşamada kurumlar güçlü bir erişim yönetim sistemine ihtiyaç duymaktadırlar. Bahsedilen sebeplerden ötürü çalışmamızda uzaktan bağlantı ve destek süreçlerinin sanal masaüstü alt yapısı kullanarak otomatik olarak yapılabileceği bir ayrıcalıklı erişim yönetim sistemi önerilmiştir. Tasarlanan sistem ile kullanıcılara, bağlantı sağlanacak sunucuda yapılacak olan iş için en az düzeyde ayrıcalık verilmesi hedeflenmektedir. Bir sunucuya yapılan bağlantıların geriye dönük takibinin rahatlıkla yapılabilmesi için, çalışma sonucu önerilmiş olan ayrıcalıklı erişim yönetim uygulamasına güçlü bir kayıt defteri sistemi (log) eklenmiştir. Eklenecek olan bu kayıt sistemi sayesinde önermiş olduğumuz sistem veri madenciliği ve iş zekâsı gibi analizlere de uyumlu olacaktır. Bahsedilen tüm özelliklerin yanı sıra önerilen sistemin ölçeklenebilir ve mikro-servis tabanlı olması, literatürde var olan yöntemlerden farklılık göstermesini sağlamaktadır.Öğe Efficient and Scalable Broker Design for the Internet of Things Environments(IEEE, 2020) Gormez, Yasin; Arslan, Halil; Kelek, Omer FarukIn line with recent requirements, many institutions and organizations have started to need IoT devices. Number of used IoT devices increased because of the increasing need. These devices can collect huge amounts of data from a wide range of sensors. This increase in the number of data brings along the problem of how the data should be collected and processed. Relational databases and standard methods are insufficient for storing and processing these huge data. Therefore, in this study, a Broker that uses NoSql database to store data, indexing the short time data with Elasticsearch to increase instantaneous processing speed, can work with multiple copies thanks to virtualization, and provide powerful user interface thanks to Kibana is proposed. This proposed broker has been tested at one of our airports on low-energy bluetooth data and was able to transmit a maximum of 68,000 data per second with the determined server.Öğe Estimating vulnerability metrics with word embedding and multiclass classification methods(Springer, 2024) Kekul, Hakan; Ergen, Burhan; Arslan, HalilCyber security has an increasing importance since the day when information technologies are an invariable part of modern human life. One of the fundamental areas of cyber security is the concept of software security. Security vulnerabilities in software are one of the main reasons for the exploitation of information systems. For this reason, it has been systematically reported, analyzed and classified for a long time, with a protocol established between the states and the stakeholders of the issue at the level. All these processes are carried out manually by humans today. This situation causes errors and delays caused by human nature. Therefore, the current study aims to help the experts and increase the accuracy of the analysis results by speeding up the processes. To achieve this goal, a model is proposed that uses technical explanations of security reports written in natural language. Our model basically proposes a method that uses word embedding approaches and multi-class classification algorithms from natural language processing techniques. In order to compare the proposed model more accurately, the NVD database, which is open to everyone and accepted as a reference, was chosen. In addition, previous studies in the literature and the model we propose were compared. In order for the results of the compared models to be analyzed more accurately, our model was trained with the data sets of the studies it was compared and the results were presented clearly. The proposed method showed estimation success in the range of 87.34-96.25% for CVSS 2.0 metrics, and in the range of 84-90% for CVSS 3.1. This study, in which different word embedding and classification algorithms are used together, is one of the limited studies on the latest version of the official scoring system used for classification of software security vulnerabilities. Moreover, it is the most comprehensive and original study in its field due to the size of the dataset it uses and the number of databases evaluated.Öğe Examining of Single Sign on Protocols and A Model of Business Application(IEEE, 2017) Arslan, Halil; Karki, H. Dogan; Yuksek, A. Gurkan; Kaynar, OguzUsers have to require authentication with many times different a set of username and password which access various service providers and applications in their daily task and on social life. In this case, the user must need to memorize many pair of a set of username and password. This one is then to enforce the users using ordinary/same passwords or to keep note of passwords somewhere. It is a problem as a secret of private user information on social life which to generate more crucial problems for a business applications. To get rid of this problem, a single sign on (SSO) is suggested. SSO describing a set of username and password maintain multiply passwords to access for different service providers and applications. In this study, we argued out the prevalent and the current issue of SSO protocols in literature and CAS which is one of the SSO protocols, is used to examine a model of business application.Öğe Farklı Vektörleştirme ve Ön işlem Yöntemleri ile Talep Sınıflandırma(2022) Arslan, Halil; Dadaş, İbrahim Ethem; Işık, Yunus EmreFirmalarda, ihtiyaçlara yönelik gelen taleplerin doğru şekilde işlenmesi hem iş sürecini hızlandırır hem de ortaya çıkabilecek sorunları bertaraf eder. Geliştirme, destek, sorun çözme gibi farklı konulardaki taleplerin, verimli ve doğru kişilerce çözülmesi için öncelikle ilgili alt departmana yönlendirilmesi gerekir. Yönlendirmeler belirli kişilerce elle gerçekleştirilebilir. Ancak firma büyüklüğüyle doğru orantılı olarak gelen talep sayısının çok olması süreci zorlaştırıp zaman kaybına yol açmaktadır. Özellikle bilişim sektöründe hizmet veren kurumsal firmalarda taleplerin otomatik olarak alt-departmanlara aktarılabilmesi, işin verimliliğinin ciddi şekilde arttırabilir. Bu ihtiyacın giderilmesi içi metni işleyerek içerisinden kolaylıkla bilgi çıkarımını sağlayabilen metin madenciliği ve makine öğrenmesi yöntemleri kullanılabilir. Çalışmamızda, Detaysoft Danışmanlık firmasına ait gelen taleplerin doğru şekilde alt departmana yönlendirilmesini sağlayan bir sistem önerilmiştir. Sistem performansının ölçülebilmesi amacıyla gerçek müşteri taleplerinden oluşan 2103 veri toplanmış ve işaretlenmiştir. Toplanan verilerin varsayımlardan bağımsız olarak doğru şekilde işaretlenmesi için de veriye göre sınıf etiketlerinin belirlendiği temellendirilmiş teoriden faydalanılmıştır. Ham metinlerin vektörleştirilmesi için kelime çantası ve türevlerinin (TF, TFIDF) yanı sıra GloVe ve Word2Vec gibi kelime gömme yöntemleri de denenmiş ve hangi vektörleştirme yönteminin daha başarılı olduğu irdelenmiştir. Ayrıca gereksiz kelimelerin ve sadece kelime köklerinin kullanılmasının talep sınıflandırmaya etkileri analiz edilmiştir. Yapılan analizler sonucunda SVM algoritmasını kullanan modellerin %79 gibi iyi sayılabilecek bir başarım ile gelen talebi doğru şekilde sınıflandırabildiği gözlemlenmiştir. Elde edilen sonuçların, talep sınıflandırma konularındaki gelecek çalışmalara hem vektörleştirme hem de ön işlem süreçleriyle alakalı ışık tutması beklenmektedir.Öğe Feature Selection Methods in Sentiment Analaysis(IEEE, 2017) Kaynar, Oguz; Arslan, Halil; Gormez, Yasin; Demirkoparan, FerhanIn today's technology, people are starting to share their opinions, ideas and feelings through many mediums because the internet is used extensively by every segment. These shares have become an important source of work on sentiment analysis and have led to increased work on this field. The sentiment analysis is simply to determine whether the emotion is included or not, and to determine whether the emotion is positive, negative, or neutral. In this study, chi-square, information gain, gain ratio, gini coefficient, oneR and reliefF methods are applied on the data sets according to the contents of movie comments and the obtained data sets are classified by Support Vector Machines (SVM). As a result of the application, it has been observed that the feature selection methods improve the results of sentiment analysis.Öğe Machine Learning and Event-Based User and Entity Behavior Analysis(Institute of Electrical and Electronics Engineers Inc., 2024) Önal, Vedat; Arslan, Halil; Görmez, YasinWith the widespread use of technology, the concept of cybersecurity frequently occupies the agenda of companies. The resistance of institutions against external attacks such as malware, denial of service, and zero-day vulnerabilities is increasing day by day, but the defense of institutions against internal threats carried out by malicious or unconscious employees has not reached the desired levels. User and entity behavior analysis, proposed to solve this problem, aims to find abnormal behavior by analyzing the daily behavior of employees. In this study, a user and entity behavior analysis model that can work in harmony with companies' security information and event management systems is proposed. In this context, firstly, the activities performed by the employees while using Windows operating systems were collected using the Wazuh application. The dataset created with the sliding window method was trained with nine different classification algorithms, and the accuracy, F1-score, sensitivity, and false-negative rate values of the models were calculated. As a result of the analysis, it was observed that the most successful results were obtained with Random Forest, k-nearest neighbor, and Bagging Methods. © 2024 IEEE.
