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Öğe Blockchain-Based KYC Model for Credit Allocation in Banking(Institute of Electrical and Electronics Engineers Inc, 2024) Karadağ, Bulut; Halim Zaim A.; Akbulut, AkhanThe implementation of the Know Your Customer (KYC) strategy by banks within the financial sector enhances the operational efficiency of such establishments. The data gathered from the client during the KYC procedure may be applied to deter possible fraudulent activities, money laundering, and other criminal undertakings. The majority of financial institutions implement their own KYC procedures. Furthermore, a centralized system permits collaboration and operation execution by multiple financial institutions. Aside from these two scenarios, KYC processes can also be executed via a blockchain-based system. The blockchain's decentralized network would be highly transparent, facilitating the validation and verification of customer data in real-time for all relevant stakeholders. In addition, the immutability and cryptography of the blockchain ensure that client information is secure and immutable, thereby eradicating the risk of data breaches. Blockchain-based KYC can further improve the client experience by eliminating the requirement for redundant paperwork and document submissions. After banks grant consumers loans, a blockchain-based KYC system is proposed in this study to collect limit, risk, and collateral information from them. The approach built upon Ethereum grants financial institutions the ability to read and write financial data on the blockchain network. This KYC method establishes a transparent, dynamic, and expeditious framework among financial institutions. In addition, solutions are discussed for the Sybil attack, one of the most severe problems in such networks.Öğe Cyberbullying detection through deep learning: A case study of Turkish celebrities on Twitter(IOS Press BV, 2023) Karadağ, Bulut; Akbulut, Akhan; Zaim, Abdul HalimOne of the ways that celebs maintain their fame in the modern era is by posting updates and photos to social media platforms like Twitter, Instagram, and Facebook. Comments left on their posts, however, expose them to cyberbullying. Cyberbullying, as a form of electronic device-based harassment, negatively impacts the lives of individuals. Thirty famous people from the fields of acting, art, music, politics, sports, and writing were chosen for this research. These notable figures include the top five Twitter followers of Turkey in each demographic. Between December 2019 and December 2020, comment responses for each celebrity were collated. Using the Deep Learning model, we were able to detect abuse content with an accuracy of 89%. Additionally, the percentage of celebrities exposed to cyberbullying by group was presented.Öğe Deep learning approaches for phantom movement recognition(Institute of Electrical and Electronics Engineers Inc., 2019) Akbulut, Akhan; Aşçı, Güven; Güngör, Feray; Tarakcı, Ela; Aydın, Muhammed Ali; Zaim, Abdül HalimPhantom limb pain has a negative effect on the life of individuals as a frequent consequence of limb amputation. The movement ability on the lost extremity can still be maintained after the amputation or deafferentation, which is called the phantom movement. The detection of these movements makes sense for cybertherapy and prosthetic control for amputees. In this paper, we employed several deep learning approaches to recognize phantom movements of the three different amputation regions including above-elbow, below-knee and above-knee. We created a dataset that contains 25 healthy and 16 amputee participants' surface electromyography (sEMG) readings via a wearable device with 2-channel EMG sensors. We compared the results of three different deep learning methods, respectively, Multilayer Perceptron, Convolutional Neural Network, and Recurrent Neural Network with the accuracies of two well-known shallow methods, k Nearest Neighbor and Random Forest. Our experiments indicate, Convolutional Neural Network-based model achieved an accuracy of 74.48% in recognizing phantom movements of amputees. © 2019 IEEE.Öğe Deep Learning Approaches for Predictive Masquerade Detection(Hindawi Limited, 2018) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimIn computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly. © 2018 Wisam Elmasry et al.Öğe A design of an integrated cloud-based intrusion detection system with third party cloud service(De Gruyter, 2021) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimAlthough cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.Öğe Empirical study on multiclass classification-based network intrusion detection(Blackwell Publishing Inc., 2019) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimEarly and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short-term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well-known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL-KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach. © 2019 Wiley Periodicals, Inc.Öğe Energy and distance factor based routing protocol for wireless sensor networks using mobile agents(2011) Akbulut, Akhan; Parmaksızoğu, Cihangir; Zaim, Abdül Halim; Yılmaz, GürayThe wireless sensor networks are networks of compact micro-sensors for data acquisition or monitoring some environment characteristics, such as temperature, sound, vibration, pressure and motion. These sensors are embedded devices capable of data communication. In many applications, sensor nodes are distributed or deployed over a geo-graphically large area. Due to their structure, data of measured values must be transferred among stations through these sensor nodes. For this reason a successful, energy efficient, fault tolerant routing protocol should be implemented to pre-vent data loss and other challenges within limited energy levels. This paper presents an agent based routing algorithm for wireless sensor networks, based on the selection of the idea of active nodes. Our proposed routing algorithm is related with energy and distance factors of each nodes. The main objective is to increase the lifetime of a sensor network while not compromising data delivery. Critical tasks such as measuring, analyzing and monitoring of energy levels of nodes are handled by these autonomous mechanisms. © 2011 IEEE.Öğe Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic(Elsevier B.V., 2020) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimThe prevention of intrusion is deemed to be a cornerstone of network security. Although excessive work has been introduced on network intrusion detection in the last decade, finding an Intrusion Detection Systems (IDS) with potent intrusion detection mechanism is still highly desirable. One of the leading causes of the high number of false alarms and a low detection rate is the existence of redundant and irrelevant features of the datasets, which are used to train the IDSs. To cope with this problem, we proposed a double Particle Swarm Optimization (PSO)-based algorithm to select both feature subset and hyperparameters in one process. The aforementioned algorithm is exploited in the pre-training phase for selecting the optimized features and model's hyperparameters automatically. In order to investigate the performance differences, we utilized three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Deep Belief Networks (DBN). Furthermore, we used two common IDS datasets in our experiments to validate our approach and show the effectiveness of the developed models. Moreover, many evaluation metrics are used for both binary and multiclass classifications to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. Experimental results show a significant improvement in network intrusion detection when using our approach by increasing Detection Rate (DR) by 4% to 6% and reducing False Alarm Rate (FAR) by 1% to 5% from the corresponding values of same models without pre-training on the same dataset. © 2019Öğe Identification of phantom movements with an ensemble learning approach(Elsevier Ltd, 2022) Akbulut, Akhan; Gungor, Feray; Tarakci, Ela; Aydin, Muhammed Ali; Zaim, Abdul Halim; Catal, CagatayPhantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.Öğe LWE: An Energy-Efficient Lightweight Encryption Algorithm for Medical Sensors and IoT Devices(İstanbul Ticaret Üniversitesi, 2020) Toprak, Sezer; Akbulut, Akhan; Aydın, Muhammed Ali; Zaim, Abdül HalimIn today’s world, systems generate and exchange digital data frequently and face a much broader range of threats than in the past. Within the context of this unsafe ecosystem, it is crucial to protect the data in a quick and secure way. In this paper, it is proposed that a lightweight block cipher algorithm called LWE in the purpose of having an encryption algorithm that is light enough for restricted/limited hardware environments and secure enough to endure primal cryptanalysis attacks. The length of blocks to be encrypted is set to 64 bits and the key length is defined as 64 bits. It is targeted for IoT systems with low-end microcontrollers and body sensor area devices. The performance and security aspects of LWE are evaluated with well-known algorithms and it is observed that LWE can establish a basic security baseline for transmitting raw data without creating a heavy load on the network infrastructure.Öğe Mobil cihazlarda güvenlik – tehditler ve temel stratejiler(İstanbul Ticaret Üniversitesi, 2016) Karataş, Gözde; Akbulut, Akhan; Zaim, Abdül HalimTüketici elektroniğinin günümüzdeki en yaygın hali mobil cihazların kullanımıdır. Bu alandaki teknolojik gelişmeler, hayatımızın her alanına etki edecek şekilde artmakta ve yaşamımıza yön vermektedir. Artık bilgisayarlarla aynı donanımsal özelliklere sahip olabilen mobil cihazların kullanımı sadece iletişim amacıyla kalmayıp, internet kullanımı, iş, hobi ve sağlık alanlarındaki uygulamaları ile zenginlemiştir. Artan kullanım oranı ile bilgi ve iletişim güvenliğine daha fazla ihtiyaç duyulmaya başlanan bu cihazlara yönelik yapılan saldırılar karşısında taşınan bilgilerin güvenliğinin sağlanması gerekliliği ortaya çıkmaktadır. Mobil cihazlardaki güvenlik açıkları ve kötücül yazılım barındıran uygulamaların son kullanıcı tarafından yüklemesi ile kişisel bilgi ve haberleşme güvenliğini tehdit eden durumlar oluşmaktadır. Bu çalışmada, mobil uygulamalarda bulunan güvenlik açıkları, saldırı ve bu sorunlara ilişkin alınan önlemler anlatılmaktadır. Sadece son kullanıcıya yönelik tavsiyeler değil, aynı zamanda uygulama geliştiriciler için de dikkat edilmesi gereken hususlar özetlenmiştir. Son kullanıcıların, mobil sistemlerin saldırı yöntemlerine dair temel bilgileri öğrenmesi ile kişisel güvenliğin arttırılabileceği değerlendirilmektedir.Öğe Mobile visual acuity assessment application: AcuMob(Istanbul University, 2017) Akbulut, Akhan; Aydın, Muhammed Ali; Zaim, Abdül HalimThis paper presents a mobile healthcare (mHealth) system for estimation of visual impairment that provides easiness by specifying the degree of an eye as orthoscopes. Our proposed system called AcuMob which is an Android based mobile application aimed to be used by patients who have myopia. In the crowd society, our proposed app will be implemented faster than the traditional ophthalmologic examination treatments as an alternative. Because AcuMob can be used in everywhere in any time slot, it is offered in the area where the ophthalmologist is not available. The system is developed with using Xamarin framework and voice commands are used to interact with mobile app. Some preferable letters that are suggested by the ophthalmologists were used in the system. The letter categories are specified according to letters' sizes. In the start-up screen, the biggest letter is demonstrated and if the user responds correct answer, the letter's size is being smaller. However, if the user says wrong answer three times consecutively, eyesight ratio is produced by the system to the user referencing to Snellen Chart's information. This article has aimed at making a prediction about the visual impairment's degree. Thanks to AcuMob, people can get idea about their visual acuity without consulting to an eye medical doctor (MD). For the evaluation of systems' reliability, field tests were performed at Bayrampaşa Göz Vakfi Hospital in Istanbul with two ophthalmologist specialists. At the end of trials, the actual diagnosed degrees and the equivalent degree of eyesight ratios according to Snellen Chart's information is compared and the success rates are shown. The system achieved at the 65% of average success rate, which can give users an idea about current condition of their visions.Öğe A Review on Blockchain Applications in Fintech Ecosystem(Institute of Electrical and Electronics Engineers Inc., 2022) Karadağ, Bulut; Akbulut, Akhan; Zaim, Abdul HalimThe term fintech started to become popular from the 90s. With the rapid development of technology and the widespread use of the internet, Fintech has become a sector in itself, especially since 2004. Within the framework of Fintech, there have been a number of advances in ATM, credit cards, debit cards, mobile transactions, internet banking and digital banking infrastructure and transactions. The emergence of Bitcoin in 2008 caused us to hear the term blockchain frequently, and the path of blockchain technology intersected with fintech. The decentralization of the blockchain, thanks to its distributed ledger structure, made it possible to make Bitcoin transfers without intermediaries. After Bitcoin, the emergence of crypto assets like Ethereum opened the way for these transactions to be programmable as an infrastructure. Programmable blockchain infrastructures have started to be used not only in financial transactions, but also in sectors such as health, supply chain, education and insurance. There are different academic studies on applications related to these sectors. However, there is no such a review that includes them all together for finance. In this study, blockchain applications in the fintech ecosystem were investigated and included in a single study. In particular, it was explained in which business it was used and in which business there was a market volume. In addition, possible future blockchain applications were also mentioned.Öğe Stacking-based ensemble learning for remaining useful life estimation(Springer, 2023) Ture, Begum Ay; Akbulut, Akhan; Zaim, Abdul Halim; Catal, CagatayExcessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.Öğe Techniques for apply predictive maintenance and remaining useful life: A systematic mapping study(Bilecik Şeyh Edebali Üniversitesi, 2021) Ay Türe, Begüm; Akbulut, Akhan; Zaim, Abdul HalimWith prognostic activities, it is possible to predict the remaining useful life (RUL) of industrial systems with high accuracy by following the current health status of devices. In this study, we have collected 199 articles on predictive maintenance and remaining useful life. The aim of our systematic mapping study is to determine which techniques and methods are used in the areas of predictive maintenance and remaining useful life. Another thing we aim is to give an idea about the main subject to the researchers who will work in this field. We created our article repository by searching databases such as IEEE and Science Direct with certain criteria and classified the articles we obtained. By applying the necessary inclusion and exclusion criteria in the article pool we collected, the most appropriate articles were determined and our study was carried out through these articles. When we focused on the results, it was learned that the SupportVector Machine algorithm is the most preferred predictive maintenance method. Most studies aimed at evaluating the performance and calculating the accuracy of the results used the Root Mean Square Error algorithm. In our study, every method and algorithm included in the articles are discussed. The articles were examined together with the goals and questions we determined, and results were obtained. The obtained results are explained and shown graphically in the article. According to the results, it is seen that the topics of predictive maintenance and remaining useful lifetime provide functionality and financial gain to the environment they are used in. Our study was concluded by light on many questions about the application of predictive maintenance.Öğe A Wearable Device for Virtual Cyber Therapy of Phantom Limb Pain(Institute of Electrical and Electronics Engineers Inc., 2018) Akbulut, Akhan; Aşçı, Güven; Tarakçı, Ela; Aydın, Muhammed Ali; Zaim, Abdül HalimPhantom limb pain (PLP) is the condition most often occurs in people who have had a limb amputated and it is may affect their life severely. When the brain sends movement signals to the phantom limb, it returns and causes a pain. Many medical approaches aim to treat the PLP, however the mirror therapy still considered as the base therapy method. The aim of this research is to develop a wearable device that measures the EMG signals from PLP patients to classify movements on the amputated limb. These signals can be used in virtual reality and augmented reality environments to realize the movements in order to reduce pain. A data set was generated with measurements taken from 8 different subjects and the classification accuracy achieved as 90% with Neural Networks method that can be used in cyber therapies. This type of therapy provides strong visuals which make the patient feel he/she really have the limb. The patient will have great therapy session time with comparison to the other classical therapy methods that can be used in home environments. © 2018 IEEE.Öğe Wireless sensor networks for space and Solar-system missions(2011) Akbulut, Akhan; Patlar, Fatma; Zaim, Abdül Halim; Yılmaz, GürayWireless sensor networks (WSNs) are multi-hop self-organizing networks which include a huge number of nodes integrating environmental measuring, data processing and wireless communications in order to apprehend, collect and process information to achieve defined tasks. A diverse set of applications for WSNs encompassing different fields have already emerged including environmental applications, inventory monitoring, military applications, intrusion detection, health applications, motion tracking, machine malfunction detection and etc. Among these application areas the use of WSNs can adapted to Space and Solar-system missions. In the last years, space-based WSNs have gained increasing attention from both the research communities and companies involved in space research. This paper outlines the usage of a space-based wireless sensor networks (SB-WSNs), which applies the concept of terrestrial wireless sensor networks to the space. © 2011 IEEE.
