Recently, serious damages have occurred in e-commerce applications due to rapidly increasing data leaks and end-user vulnerabilities. Although the source of the vulnerabilities is different, attacks result in the theft of unsafe data. In particular, the theft of credit card information reveals a financial loss. In this study, a blockchain-based secure storage model has been developed in order to prevent the theft of credit card information in e-commerce applications as a result of a possible data leak. In the sample e-commerce application developed with ASP.NET, data other than credit cards are stored. Credit card data is transmitted to the blockchain over the API with SSL protection in the e-commerce application. The blockchain model was developed using MongoDB with the BigchainDB framework. The data in each block of the blockchain is encrypted with Advanced Encryption Standard (AES) 256 bits. The data integrity of the block is provided by the SHA256 algorithm. it is aimed to protect credit card data from a possible data leak with the proposed BigchainDB-based blockchain model.

Additive Manufacturing technology is one of the technologies that is changing the manufacturing industry. It has revealed some advantages over traditional manufacturing methods with this technology. With the advancement of information technologies, new approaches focusing on cost and improvement have begun to be adopted in the manufacturing industry. One such method is digital twin technology. A digital twin is frequently referred to as a digital replication of a physical system. Digital twins provide data and models to support the operation of design and manufacturing processes, as well as troubleshooting, diagnostics, and problem-solving. Various sensors are required to monitor the status of physical systems and transfer data to digital systems. Some of these Internet of Things-compatible sensors are already in production machines, but others can be added later. In the study, an Internet of Things-based system was proposed for the creation of digital twins using a virtual environment, and a digital twin simulation was created in order to bring the benefits of digitalization to production systems. The digital twin is modeled in the Matlab Simulink environment to perform binary classification to detect abnormal physical conditions that have the potential to disrupt the operation of the additive manufacturing machine and affect the quality of the manufacturing part. By generating a digital twin from real machine data, the proposed system will be able to detect errors.

The widespread use of the Internet and the increase in Internet of Things (IoT) equipment paved the way for the development of user-friendly systems. The inclusion of many electronic systems in the IoT ecosystem, especially in homes, has improved remote control and monitoring features. In general, within the concept of smart home, voice, control and joint movements are used as command center and WIFI, Bluetooth, Zigbee and GSM etc. technologies are used for communication. Regular monitoring of some health problems of individuals occurs in some situations that require instant intervention. In this context, individuals in need of home care or under surveillance at home are checked with Internet of Healthcare Things equipment in the company of experts. The rapid spread of the IoT ecosystem has also increased data production. Especially sensitive health data is at the beginning of critical data and requires security measures to be taken. In this study, the IoT devices used for home patient care have been evaluated for the sources of data leaks and possible security measures that may be experienced in the process from the data owner to the data storage stage. In order to identify possible risks and threats, 4 different target scenarios were created. These scenarios include home internet connection resources, data transfer, data storage and access. 8 different attacks (Deauth, DDOS, brute force, hashcat, Man-in-the-middle, Injection, Short Address Attack, Smart Contract Overflow) were applied to these possible scenarios where data leakage could occur. In addition, recently, blockchain applications and smart contract transmissions are preferred for data security. Among the attack scenarios, Short Address Attack and Smart Contract Overflow are attack methodologies used for blockchain security. In particular, denial of service was encountered in all attacks on wireless networks. Configuration errors, wrong product selection, use of weak passwords and default configurations in the IOT ecosystem seem to be the main sources of data leaks. As a result, the study includes possible attacker scenarios and possible vulnerabilities have been extracted within the scope of real scenarios. In addition, the measures to be taken against these vulnerabilities were evaluated and recommendations were given to take maximum security measures to prevent data leaks from within the IoT ecosystem.