Explainable Credit Card Fraud Detection with Image Conversion

The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed

Optimal reusable rocket landing guidance: a cutting-edge approach integrating scientific machine learning and enhanced neural networks

This study presents an innovative approach that utilizes scientific machine learning and two types of enhanced neural networks for modeling a parametric guidance algorithm within the framework of ordinary differential equations to optimize the landing phase of reusable rockets. Our approach addresses various challenges, such as reducing prediction uncertainty, minimizing the need for extensive training data, improving convergence speed, decreasing computational complexity, and enhancing prediction accuracy for unseen data. We developed two distinct enhanced neural network architectures to achieve these objectives: Adaptive (AQResNet) and Rowdy Adaptive (RAQResNet) Quadratic Residual Neural Networks. These architectures exhibited outstanding performance in our simulations. Notably, the RAQResNet model achieved a validation loss approximately 300 times lower than the standard architecture with an equal number of trainable parameters and 50 times lower than the standard architecture with twice the number of trainable parameters. Furthermore, these models require significantly less computational power, enabling real-time computation on modern flight hardware. The inference times of our proposed models were measured in approximately microseconds on a single-board computer. Additionally, we conducted an extensive Monte Carlo analysis that considers a wide range of factors, extending beyond aerodynamic uncertainty, to assess the robustness of our models. The results demonstrate the impressive adaptability of our proposed guidance policy to new conditions and distributions outside the training domain. Overall, this study makes a substantial contribution to the field of reusable rocket landing guidance and establishes a foundation for future advancements

Development of UCAV fleet autonomy by reinforcement learning in a wargame simulation environment

In this study, we develop a machine learning based fleet autonomy for Unmanned Combat Aerial Vehicles (UCAVs) utilizing a synthetic simulation-based wargame environment. Aircraft survivability is modeled as Markov processes. Mission success metrics are developed to introduce collision avoidance and survival probability of the fleet. Flight path planning is performed utilizing the proximal policy optimization (PPO) based reinforcement learning method to obtain attack patterns with a multi-objective mission success criteria corresponding to the mission success metrics. Performance of the proposed system is evaluated by utilizing the Monte Carlo analysis in which a wider initial position interval is used when compared to the defined interval in the training phase. This provides a preliminary insight about the generalization ability of the RL agen

Physics guided deep learning for data-driven aircraft fuel consumption modeling

This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy

Cooperative planning for an unmanned combat aerial vehicle fleet using reinforcement learning

In this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and approximate time-to-go terms are used in the reward function to obtain cooperative motion while ensuring no-fly-zones (NFZs) and time-of-arrival constraints. Proximal policy optimization (PPO) algorithm is used in the training phase of the RL agent. System performance is evaluated in two different cases. In case 1, the warfare environment contains only the target area, and simultaneous arrival is desired to obtain the saturated attack effect. In case 2, the warfare environment contains NFZs in addition to the target area and the standard saturated attack and collision avoidance requirements. Particle swarm optimization (PSO)-based cooperative path planning algorithm is implemented as the baseline method, and it is compared with the proposed algorithm in terms of execution time and developed performance metrics. Monte Carlo simulation studies are performed to evaluate the system performance. According to the simulation results, the proposed system is able to generate feasible flight paths in real-time while considering the physical and operational constraints such as acceleration limits, NFZ restrictions, simultaneous arrival, and collision avoidance requirements. In that respect, the approach provides a novel and computationally efficient method for solving the large-scale cooperative path planning for UCAV fleets

A new nonlinear lifting-line method for aerodynamic analysis and deep learning modeling of small UAVs

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles (UAVs). First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and poststall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics (CFD). This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of UAVs. The major novel feature of this model is that it can predict the aerodynamic properties of UAV configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of a UAV over a wide angle of attack range on the order of milliseconds, whereas CFD solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example

Developıng A Lambda Archıtecture For Bıg Data Processıng Applıcatıons

Büyük Veri teknolojileri ilk olarak kullanılmaya başlandığından beri ihtiyaç duyulan amaçlar doğrultusunda değişim geçirmiştir. Başlangıçta sadece duran veri işleme sistemleri kullanılıyorken zamanla akışkan verilerin de işlenebilmesi ihtiyacı doğmuştur. Günümüzde ise büyük verilerden fayda sağlanabilmesi için bu ihtiyaç, duran ve hareketli verilerin birlikte işlenebilmesini gerektirmektedir. Bu konuda halen olgunlaşmış bir teknoloji olmasa da teorik ve konsept bazında yayınlanan çalışmalar ilgi çekicidir. Bu çalışmada henüz konsept bazında, akademik olarak yeterince araştırılamamış ve pratikte yaygın olarak kullanılamayan yeni bir Büyük Veri teknolojisi olan Lamda Mimari üzerinde detaylı bir araştırma ve geliştirme faaliyeti icra edilmiştir. Üç farklı katmandan oluşan mimarinin akışkan veri, duran veri ve sunum katmanlarının, konseptin tarif ettiği şekilde tam olarak kullanılmasıyla özgün bir tasarım geliştirilmiştir. Özellikle sunum katmanında mimarinin ihtiyaçlarını karşılayacak bir teknolojinin mevcut olmaması nedeniyle bu konuya odaklanılarak, mimarinin ihtiyaç duyduğu şekilde bir katman oluşturulabilmiştir. Ayrıca duran ve akışkan veri katmanlarında gerçekleştirilecek farklı karakteristik ve nitelikteki Büyük Veri analiz işlemleri için gerekli yazılım/kod üretiminin tek bir platformda toplanması sağlanarak yeni bir mimari oluşturulmuştur. Bu mimariye gerçek zamanlı uygulama katmanı da eklenerek orijinal konseptinde olmayan bir özellik kazandırılmıştır. Gerçekleştirilen testler sonucunda tasarımın deklare edilen konseptinde olmayan yeni yetenekleri de bünyesinde barındıracak şekilde, gerçek zamanlı ve durağan verilerin birlikte işlenebildiği özgün bir Lamda Mimari tasarlanmıştır. Gerçek hayatta karşılaşılabilecek senaryolar doğrultusunda yürütülen yedi farklı testin sonucunda tasarımın Lamda Mimari olarak oldukça güvenilir olarak çalıştığı tespit edilmiştir.Big data technologies have had a need for change in line with the objectives needed to be used since the time they first began. Initially only batch processing systems had been used, but the need for real time data processing use also came into being in time. Today, however, to benefit from big data requires for this need a combination of batch and speed layers must be processed together. Even though there has been no matured technology, in terms of research on this issue published works are interesting in relation to theoretical and conceptual framework. In this research, adequate academic research has not been conducted on the basis of concept yet; there hasn't been a satisfactorily detailed and developed research on the lambda architecture, which is a big data technology being unused practically and commonly. When the three-folded batch layer, speed layer and batch processing of the structure are used as indicated by the concept, an original design has been advanced. Especially because of the absence of a technology that meets the needs of serving layer architecture, a layer could have been formed as required by the architecture by focusing on these issues. By collecting on the same platform the production of design and code required for various features and characteristics of batch and real time layers that are needed for the large date analyses, quite an original architecture has been established. By adding a real time layer to this architecture, a new feature that does not exist in its original concept has been gained. As a result of tests, by adding new capabilities to its structure in the declared concept of the design, original lambda architecture has been designed in which real time and batch layer can be processed together. At the end of seven different tests in the direction of scenarios that can be faced in real life, it has been identified that as an architect of lambda, the design has been pretty reliable

Quadratic Residual Multiplicative Filter Neural Networks for Efficient Approximation of Complex Sensor Signals

In this research, we present an innovative Quadratic Residual Multiplicative Filter Neural Network (QRMFNN) to effectively learn extremely complex sensor signals as a low-dimensional regression problem. Based on this novel neural network model, we introduce two enhanced architectures, namely FourierQResNet and GaborQResNet. These networks integrate the benefits of quadratic residual neural networks, multiplicative filter neural networks, and several filters in signal processing to effectively capture complex signal patterns, thereby addressing issues associated with convergence speed, precision, and spectral bias. These architectures indicate effectiveness in reducing spectral bias, thereby improving the accuracy of signal approximation. After conducting comprehensive experiments on ten very complex test signals from diverse application domains, the proposed architectures have demonstrated superior ability in approximating intricate sensor signals and mitigating spectral bias effectively. The numerical results of the experiments reveal that FourierQResNet and GaborQResNet exhibit excellent performance compared to other existing neural network architectures and models in accurately estimating complicated sensor signals, with admiringly small errors. In addition, the findings emphasize the importance of mitigating spectral bias in order to achieve reliable learning from sensor data. The implications of these results are significant in various domains that require precise and reliable sensor data analysis, including healthcare, environmental monitoring, aviation, IoT applications, and industrial automation. This research significantly advances the field of sensor signal approximation and opens new avenues for enhancing data interpretation reliability and accuracy in complex signal environments

A Quantitative CVSS-Based Cyber Security Risk Assessment Methodology For IT Systems

International Carnahan Conference on Security Technology(2017 : Madrid; Spain)IT system risk assessments are indispensable due to increasing cyber threats within our ever-growing IT systems. Moreover, laws and regulations urge organizations to conduct risk assessments regularly. Even though there exist several risk management frameworks and methodologies, they are in general high level, not defining the risk metrics, risk metrics values and the detailed risk assessment formulas for different risk views. To address this need, we define a novel risk assessment methodology specific to IT systems. Our model is quantitative, both asset and vulnerability centric and defines low and high level risk metrics. High level risk metrics are defined in two general categories; base and attack graph-based. In our paper, we provide a detailed explanation of formulations in each category and make our implemented software publicly available for those who are interested in applying the proposed methodology to their IT systems.This work was supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK), TEYDEB 1501, Grant No: 3160047

Bilgisayar Bilimlerinde Güncel Konular

Günümüzün teknoloji çağı olarak nitelendirilmesinde şüphe yok ki bilgisayar bilimlerinin payı oldukça fazladır. Bilgisayar bilimi kavramı ortaya çıktığı andan itibaren hızlı bir gelişim göstermiş; günümüzde de ivmesi artarak devam etmektedir. “Bilgisayar Bilimlerinde Güncel Konular” isimli bu kitap on beş farklı bölümden oluşmaktadır. Kitabın bölümleri titiz bir çalışma neticesinde belirlenmiştir. Her biri alanında uzman, akademisyen ve mühendislerden oluşan yazar grubunun itinalı çalışması neticesinde ortaya çıkan bu eser, Bilgisayar Mühendisliğinde Güncel Konular, Bilgisayar Bilimlerinde Güncel Konular, Bilgisayar Mühendisliğinde Özel Konular, Bilgisayar Mühendisliğine Giriş, Yazılım Mühendisliğine Giriş ve Kariyer Planlama dersleri için kaynak kitap niteliğindedir. Eser, içerik itibarıyla bilgisayar ve yazılım Mühendisliği Bölümü öğrencileri başta olmak üzere lise öğrencilerinden akademik kariyer planı yapan lisansüstü öğrencilere kadar bilişim ve bilgisayar alanına ilgi duyan herkesi muhatap almaktadır.</p