Дослідження моделей розгортання хмарних технологій для банківських інформаційних систем

The object of research is banking information technology (IT). One of the most problematic issues is the low efficiency of using hardware resources and, as a result, high costs and time spent on maintaining and developing banking information systems (IS). The use of cloud technologies, especially with the use of the public cloud deployment model, can greatly enhance the economic efficiency of banking IT. In addition, there is an increase in the availability, flexibility and scalability of banking IT, as well as the time to market, (TTM). In the course of the study, quantitative and qualitative indicators of the functioning of banking IS were used.An analysis of modern approaches to building a service-oriented architecture of banking IS based on cloud technologies was conducted in scope of the research. The article describes the architectural solution of information technologies for the introduction of automated banking IS taking into account the requirements of the National Bank of Ukraine and European regulators. The analysis of the main banking systems and the expediency of using different models of cloud technologies deployment are analyzed.The result obtained in quantitative parameters of the system load allows to find additional reserves for optimization of time processing of information and increase economic efficiency using the Public cloud. The greatest effect can be achieved by applying this model to the Core Banking System (CBS). In order to comply with the requirements and to take into account restrictions on the placement of client data, the article proposes a mechanism for depersonalization.This ensures the possibility of obtaining the most optimal values of indicators. Compared to similar well-known services, such as virtualization, it benefits because there is no need to purchase, or lease hardware, and the computing power can be scaled in a much wider range.Объектом исследования являются банковские информационные технологи (далее ИТ). Одним из самых проблемных мест является низкая эффективность использования аппаратных ресурсов и как следствие высокие затраты времени и бюджета на поддержку и развитие банковских информационных систем (далее ИС). Применение облачных технологий, особенно при использовании модели развертывания Public Cloud, позволяет значительно повысить экономическую эффективность банковских ИТ. Кроме этого, повышается отказоустойчивость, гибкость и масштабируемость банковских ИТ, а также показатель скорости вывода продуктов на рынок (от англ. time to market). В ходе исследования использовались количественные и качественные показатели функционирования банковских ИС.Проведен анализ современных подходов к построению сервисно-ориентированной архитектуры банковских информационных систем на основе облачных технологий. Приведено описание архитектурного решения ИТ по внедрению автоматизированных банковских ИС с учетом требований Национального Банка Украины и Европейских регуляторов. Проанализированы основные банковские ИС и целесообразность применения к ним различных моделей развертывания облачных технологий.Полученный результат в количественном выражении показателей нагрузки на систему позволяет найти дополнительные резервы для оптимизации времени обработки информации и повышения экономической эффективности за счет применения Public Cloud. Наибольшего эффекта можно достичь, применив эту модель к автоматизированному операционному дню банка (от англ. Core Banking System). Для соблюдения требований и учета ограничений по размещению клиентских данных в работе предложен механизм деперсонализации.Благодаря этому обеспечивается возможность получения наиболее оптимальных значений показателей. По сравнению с аналогичными известными сервисами, такими как виртуализация, это обеспечивает преимущество, ведь нет необходимости приобретения или аренды оборудования, а вычислительная мощность может масштабироваться в значительно более широком диапазоне.Об'єктом дослідження є банківські інформаційні технології (надалі ІТ). Одним з найбільш проблемних місць є низька ефективність використання апаратних ресурсів та як наслідок високі витрати часу та бюджету на підтримку та розвиток банківських інформаційних систем (надалі ІС). Застосування хмарних технологій, особливо за умови використання моделі розгортання Public Cloud, дозволяє значно підвищити економічну ефективність банківських ІТ. Крім цього, підвищується відмовостійкість, гнучкість та масштабованість банківських ІТ, а також показник швидкості виводу продуктів на ринок (від англ. time to market). В ході дослідження використовувалися кількісні та якісні показники функціонування банківських ІС.Проведено аналіз сучасних підходів до побудови сервісно-орієнтованої архітектури банківських ІС на основі хмарних технологій. Приведено опис архітектурного рішення ІТ з впровадження автоматизованих банківських ІС з урахуванням вимог Національного Банку України та Європейських регуляторів. Проаналізовано основні банківські ІС та доцільність застосування до них різних моделей розгортання хмарних технологій.Отриманий результат в кількісному вираженні показників навантаження на систему дозволяє знайти додаткові резерви для оптимізації часу обробки інформації і підвищення економічної ефективності за рахунок застосування Public Cloud. Найбільшого ефекту можливо досягти, застосувавши цю модель до автоматизованого операційного дня банку (від англ. Core Banking System). Для дотримання вимог та урахування обмежень щодо розміщення клієнтських даних в роботі запропонований механізм деперсоналізації.Завдяки цьому забезпечується можливість отримання найбільш оптимальних значень показників. У порівнянні з аналогічними відомими сервісами, такими як віртуалізація, це забезпечує перевагу, адже немає необхідності придбання чи оренди обладнання, а обчислювана потужність може масштабуватися в значно ширшому діапазоні

EMPOWERING, a smart Big Data framework for sustainable electricity suppliers

This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received about energy bills and offering online tools. The main contributions of EMPOWERING are the creation of a novel workflow in the electricity utility sector regarding the implementation of data analytics for their customers and the fast implementation of data-mining techniques in massive datasets within a Big Data platform to achieve scalability. The results obtained show that EMPOWERING can be of use for customers of electrical suppliers by changing their energy habits to decrease consumption and so increase environmental sustainability

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

A Study On API Security Pentesting

Application Programming Interfaces (APIs) are essential in the digital realm as the bridge enabling seamless communication and collaboration between diverse software applications. Their significance lies in simplifying the integration of different systems, allowing them to work together effortlessly and share data. APIs are used in various applications, for example, healthcare, banks, authentication, etc. Ensuring the security of APIs is critical to ensure data security, privacy, and more. Therefore, the security of APIs is not only urgent but mandatory for pentesting APIs at every stage of development and to catch vulnerabilities early. The primary purpose of this research is to provide guidelines to help apply existing tools for reconnaissance and authentication pentesting. To achieve this goal, we first introduce the basics of API and OWASP\u27s Top 10 API security vulnerabilities. Secondly, we propose deployable scripts developed for Ubuntu Debian Systems to install pentesting tools automatically. These scripts allow future students to participate in API security courses and conduct API security pentesting. API security pentesting, regarding reconnaissance and authentication, is discussed based on the configured system. For reconnaissance, passive and active approaches are introduced with different tools for authentication, including password-based authentication brute-forcing, one-time password (OTP) brute-forcing, and JSON web token brute force

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

Selected Computing Research Papers Volume 5 June 2016

An Analysis of Current Computer Assisted Learning Techniques Aimed at Boosting Pass Rate Level and Interactivity of Students (Gilbert Bosilong) ........................................ 1 Evaluating the Ability of Anti-Malware to Overcome Code Obfuscation (Matthew Carson) .................................................................................................................................. 9 Evaluation of Current Research in Machine Learning Techniques Used in Anomaly-Based Network Intrusion Detection (Masego Chibaya) ..................................................... 15 A Critical Evaluation of Current Research on Techniques Aimed at Improving Search Efficiency over Encrypted Cloud Data (Kgosi Dickson) ........................................ 21 A Critical Analysis and Evaluation of Current Research on Credit Card Fraud Detection Methods (Lebogang Otto Gaboitaolelwe) .......................................................... 29 Evaluation of Research in Automatic Detection of Emotion from Facial Expressions (Olorato D. Gaonewe) ......................................................................................................... 35 A Critical Evaluation on Methods of Increasing the Detection Rate of Anti-Malware Software (Thomas Gordon) ................................................................................................ 43 An Evaluation of the Effectiveness of the Advanced Intrusion Detection Systems Utilizing Optimization on System Security Technologies (Carlos Lee) ............................ 49 An Evaluation of Current Research on Data Mining Techniques in Decision Support (Keamogetse Mojapelo) ...................................................................................................... 57 A Critical Investigation of the Cognitive Appeal and Impact of Video Games on Players (Kealeboga Charlie Mokgalo) ................................................................................ 65 Evaluation of Computing Research Aimed at Improving Virtualization Implementation in the Cloud (Keletso King Mooketsane) ................................................. 73 A Critical Evaluation of the Technology Used In Robotic Assisted Surgeries (Botshelo Keletso Mosekiemang) ....................................................................................... 79 An Evaluation of Current Bio-Metric Fingerprint Liveness Detection (George Phillipson) ........................................................................................................................... 85 A Critical Evaluation of Current Research into Malware Detection Using Neural-Network Classification (Tebogo Duduetsang Ramatebele) ................................................ 91 Evaluating Indirect Detection of Obfuscated Malware (Benjamin Stuart Roberts) ......... 101 Evaluation of Current Security Techniques for Online Banking Transactions (Annah Vickerman) ....................................................................................................................... 10