Academic literature on the topic 'AES-256 Encryption'

As the internet of things and cloud computing create new technological revolutions in the field of healthcare, the issues such as security, privacy, authentication, the integrity of patient data, and medical images become sensitive. This chapter proposes a cryptographic technique for the secure storage of medical images based on biometric key generation. Cryptographic algorithms can be used to enhance the security of cloud storage systems. The proposed biometric-based encryption scheme takes the advantage of biometric features of the fingerprint and iris of the user to meet the desired security characteristics. A 256-bit bio-key is generated based on the distance between the fingerprint features and used in the advanced encryption standard (AES) for image encryption. The experiments conducted on the set of medical images also prove the effectiveness and security aspects of the proposed biometric key-based image encryption. The encryption method is also tested on the set of medical images and compared with other encryption schemes.

E-Health applications enable one to acquire, process, and share patient medical data to improve diagnosis, treatment, and patient monitoring. Despite the undeniable benefits brought by the digitization of health systems, the transmission of and access to medical information raises critical issues, mainly related to security and privacy. This paper describes a safe e-health data management system that employs the MERN stack (MongoDB, Express, React, and Node.js) and strong encryption algorithms. The solution improves security by using client-side encryption with the AES-256-CBC technique to encrypt e-health files before uploading them to MongoDB. To improve security, encrypted files and keys are stored separately. User authentication employs bcryptjs for password hashing and JSON Web Tokens (JWT) for permission, ensuring that only authorized users can access and decrypt data. The study demonstrates the efficiency of coupling MERN stack development with encryption in protecting sensitive patient information, establishing confidence, and boosting the adoption of e-health solutions.

In the evolving landscape of cybersecurity, the emergence of quantum annealers presents a formidable challenge to rely on existing cryptographic algorithms such as Secure Hash Algorithm (SHA-256) and Advanced Encryption Standard (AES). Building on our findings in cryptanalysis, our work explores the potential vulnerabilities these quantum technologies pose to existing cryptographic methods. We investigate how the transformation of cryptographic problems into quadratic unconstrained binary optimization (QUBO) format, suitable for quantum annealing, could expedite the decryption processes, thereby undermining the security of widely used algorithms. The implications for industries relying on these cryptographic standards are profound. From financial transactions to secure communications, the threat of quantum annealing necessitates a re-evaluation of current security protocols and the acceleration of developing quantum-resistant cryptographic techniques. This chapter aims to provide industry stakeholders with a comprehensive understanding of these emerging threats and outline recipes for urgent proactive measures in the face of quantum advances.

The eXtensible Markup Language (XML) has been widely adopted in many financial institutions in their daily transactions. This adoption was due to the flexible nature of XML providing a common syntax for systems messaging in general and in financial messaging in specific. Excessive use of XML in financial transactions messaging created an aligned interest in security protocols integrated into XML solutions in order to protect exchanged XML messages in an efficient yet powerful mechanism. However, financial institutions (i.e. banks) perform large volume of transactions on daily basis which require securing XML messages on large scale. Securing large volume of messages will result performance and resource issues. Therefore, an approach is needed to secure specified portions of an XML document, syntax and processing rules for representing secured parts. In this research we have developed a smart approach for securing financial XML transactions using effective and intelligent fuzzy classification techniques. Our approach defines the process of classifying XML content using a set of fuzzy variables. Upon fuzzy classification phase, a unique value is assigned to a defined attribute named “Importance Level”. Assigned value indicates the data sensitivity for each XML tag. The research also defines the process of securing classified financial XML message content by performing element-wise XML encryption on selected parts defined in fuzzy classification phase. Element-wise encryption is performed using symmetric encryption using AES algorithm with different key sizes. Key size of 128-bit is being used on tags classified with “Medium” importance level; a key size of 256-bit is being used on tags classified with “High” importance level. An implementation has been performed on a real-life environment using online banking system in Jordan Ahli Bank one of the leading banks in Jordan to demonstrate its flexibility, feasibility, and efficiency. Our experimental results of the system verified tangible enhancements in encryption efficiency, processing-time reduction, and resulting XML message sizes. Finally, our proposed system was designed, developed, and evaluated using a live data extracted from an internet banking service in one of the leading banks in Jordan. The results obtained from our experiments are promising, showing that our model can provide an effective yet resilient support for financial systems to secure exchanged financial XML messages.

The eXtensible Markup Language (XML) has been widely adopted in many financial institutions in their daily transactions. This adoption was due to the flexible nature of XML providing a common syntax for systems messaging in general and in financial messaging in specific. Excessive use of XML in financial transactions messaging created an aligned interest in security protocols integrated into XML solutions in order to protect exchanged XML messages in an efficient yet powerful mechanism. However, financial institutions (i.e. banks) perform large volume of transactions on daily basis which require securing XML messages on large scale. Securing large volume of messages will result performance and resource issues. Therefore, an approach is needed to secure specified portions of an XML document, syntax and processing rules for representing secured parts. In this research we have developed a smart approach for securing financial XML transactions using effective and intelligent fuzzy classification techniques. Our approach defines the process of classifying XML content using a set of fuzzy variables. Upon fuzzy classification phase, a unique value is assigned to a defined attribute named “Importance Level”. Assigned value indicates the data sensitivity for each XML tag. The research also defines the process of securing classified financial XML message content by performing element-wise XML encryption on selected parts defined in fuzzy classification phase. Element-wise encryption is performed using symmetric encryption using AES algorithm with different key sizes. Key size of 128-bit is being used on tags classified with “Medium” importance level; a key size of 256-bit is being used on tags classified with “High” importance level. An implementation has been performed on a real-life environment using online banking system in Jordan Ahli Bank one of the leading banks in Jordan to demonstrate its flexibility, feasibility, and efficiency. Our experimental results of the system verified tangible enhancements in encryption efficiency, processing-time reduction, and resulting XML message sizes. Finally, our proposed system was designed, developed, and evaluated using a live data extracted from an internet banking service in one of the leading banks in Jordan. The results obtained from our experiments are promising, showing that our model can provide an effective yet resilient support for financial systems to secure exchanged financial XML messages.

The section presents modern models and software tools for analysis, forecasting and management of financial risks. Mathematical approaches such as regression analysis, time series models (ARIMA, GARCH), scenario analysis, stress testing and portfolio optimization using the Markowitz model are considered. Special attention is paid to machine learning methods, including Random Forest, Gradient Boosting and neural networks, which provide high accuracy of forecasts and detection of hidden dependencies in data. The use of modular and microservice architectures is proposed for the development of systems that integrate big data analysis, optimization and visualization tools. The stages of data processing, the application of containerization (Docker, Kubernetes), as well as deployment automation (CI/CD) using cloud platforms (AWS, Azure, Google Cloud) are described in detail. Special attention is paid to security issues, including data encryption (AES-256), access control (RBAC, MFA) and system monitoring. The presented quantitative and qualitative assessments of the effectiveness of methods and models demonstrate the possibilities of reducing risks and increasing the resilience of financial systems. The chapter can become a basis for researchers, analysts and developers involved in creating financial modeling software, as well as for developing risk management strategies in changing economic conditions. The purpose of this research is to create an integrated approach to forecasting and managing financial risks by developing modern mathematical models, applying machine learning methods and implementing advanced software engineering tools. The research is aimed at improving tools for time series analysis, portfolio optimization, risk classification and forecasting using algorithms such as Random Forest, neural networks and GARCH. Particular attention is paid to the development of scalable software systems with modular and microservice architecture, integrated with cloud platforms to provide real-time processing of big data. An important aspect is ensuring the security of financial data through encryption, access control and monitoring, as well as the creation of interactive visualization tools to support decision-making. The results of the research are aimed at increasing the accuracy, efficiency and reliability of solutions in the field of financial modeling. The methodology presented in this article is based on a review of current technical and software solutions for controlling unmanned aerial vehicles (UAVs), focusing on hardware platforms, sensors, communication systems, and software. Comparative analysis of hardware platforms (FPGA, ARM, Atmel, Raspberry Pi) on key parameters: performance, flexibility, power consumption, complexity and cost. Software evaluations that include open platforms (ArduPilot, PX4, LibrePilot) and high-level control systems (Aerostack2, GAAS). Integration of sensor data using machine learning algorithms, such as the Kalman filter, to improve navigation accuracy and flight stability. Modeling of UAV energy consumption taking into account cargo weight, route length and quadratic growth due to aerodynamic drag. Analysis of multi-agent systems for drone group coordination, including trajectory modeling and motion synchronization. A graphical representation of the data that demonstrates a comparison of platforms, trajectories and energy consumption patterns The scientific novelty of the research lies in the integration of modern mathematical methods, machine learning algorithms and software engineering to solve the problems of forecasting and managing financial risks. A combination of traditional models, such as ARIMA, GARCH and regression analysis, with deep learning methods is proposed, which provides increased forecasting accuracy and adaptability to market changes. The use of microservice architecture and cloud platforms allows you to create scalable systems for processing large amounts of data in real time. An innovative approach to financial data protection has been implemented, including encryption, access control and monitoring, and interactive visualization tools have been developed that facilitate rapid analysis of results and decision-making. The research offers a comprehensive approach to risk management, focused on solving current problems in the financial sector using advanced technologies. Results. The research achieved a number of significant results that ensure increased efficiency and accuracy of financial risk forecasting. Mathematical models, in particular ARIMA and GARCH, were developed and integrated, allowing for detailed analysis of time series and assessment of financial indicator volatility. The use of machine learning algorithms, such as Random Forest, Gradient Boosting, and neural networks, ensured forecasting accuracy of up to 90% when analyzing risk portfolios and scenarios. Microservice architecture became the basis for creating scalable systems that easily adapt to changing market conditions, and cloud platforms (AWS, Azure, Google Cloud) provided high computing power and availability. Containerization using Docker and Kubernetes significantly simplified the management of system components and their integration. Interactive visualization tools, such as risk heat maps and interactive dashboards, were developed to simplify the analysis of complex financial data and facilitate informed decision-making. Ensuring data security through the implementation of encryption (AES-256), multi-level access (RBAC) and monitoring systems has increased the security of confidential information. Stress testing and scenario analysis have allowed us to assess the impact of extreme events on financial systems, develop strategies to reduce losses and ensure the stability of portfolios. The developed methods have demonstrated effectiveness in changing market conditions, confirming their value for analysts, financial institutions and software developers.