Decentralized Machine Autonomy for Manufacturing Servitization

Blockchain ecosystems are rapidly maturing and meeting the needs of business environments (e.g., industry, manufacturing, and robotics). The decentralized approaches in industries enable novel business concepts, such as machine autonomy and servitization of manufacturing environments. Introducing the distributed ledger technology principles into the machine sharing and servitization economy faces several challenges, and the integration opens new interesting research questions. Our research focuses on data and event models and secure upgradeable smart contract platforms for machine servitization. Our research indicates that with the proposed approaches, we can efficiently separate on- and off-chain data and assure scalability of the DApp without compromising the trust. We demonstrate that the secure upgradeable smart contract platform, which was adapted for machine servitization, supports the business workflow and, at the same time, assures common identification and authorization of all the participants in the system, including people, devices, and legal entities. We present a hybrid decentralized application (DApp) for the servitization of 3D printing. The solution can be used for or easily adapted to other manufacturing domains. It comprises a modular, upgradeable smart contract platform and off-chain machine, customer and web management, and monitoring interfaces. We pay special attention to the data and event models during the design, which are fundamental for the hybrid data storage and DApp architecture and the responsiveness of off-chain interfaces. The smart contract platform uses a proxy contract to control the access of smart contracts and role-based access control in function calls for blockchain users. We deploy and evaluate the DApp in a consortium blockchain network for performance and privacy. All the actors in the solution, including the machines, are identified by their blockchain accounts and are compeers. Our solution thus facilitates integration with the traditional information-communication systems in terms of the hybrid architectures and security standards for smart contract design comparable to those in traditional software engineering.

Safety and Security beyond Industry 4.0

As open standards like OPC UA were introduced to facilitate the interoperability of devices, safety and security standards were only gradually incorporated into Industry 4.0 designs. Hence, safety and security issues have eventually emerged with the growing complexity of such systems, and they need to expand outside of their premises to be integrated into larger set-ups. Typical examples thereof are power grids, whose dependability was guarded for long times by strict rules and restrictive communication protocols. Once they were exposed on the Internet, however, their vulnerability has increased to a point where their safety could be compromised. Hence, there is an urgent need to implement effective safety and security mechanisms into OPC UA to provide systems with appropriate levels of functional safety as well as data security. With increasing levels of integration, e.g. with Industry 5.0, these standards need to evolve to provide for sustainability in a globally interconnected world.

Automotive Vulnerability Analysis for Deep Learning Blockchain Consensus Algorithm

In this study, future cars are attempting self-driving around the world. However, hacking, such as ECUs in automobiles, creates problems that are directly connected to human life. Therefore, this study wrote a paper that detects anomalies in such cars by field. As a related study, the study investigated the vulnerabilities of the automobile security committee and automobile security standards and investigated the detection of abnormalities in the hacking of geo-train cars using artificial intelligence’s LSTM and blockchain consensus algorithm. In addition, in automobile security, an algorithm was studied to predict normal and abnormal values using LSTM-based anomaly detection techniques on the premise that automobile communication networks are largely divided into internal and external networks. In the methodology, LSTM’s pure propagation malicious code detection technique was used, and it worked with an artificial intelligence consensus algorithm to increase security. In addition, Unity ML conducted an experiment by constructing a virtual environment using the Beta version. The LSTM blockchain consensus node network was composed of 50,000 processes to compare performance. For the first time, 100 Grouped Tx, 500 Channels were tested for performance. For the first time, the malicious code detection rate of the existing system was verified. Accelerator, Multichannel, Sharding, Raiden, Plasma, and Trubit values were verified, and values of approximately 15,000 to 50,000 were obtained. In this paper, we studied to become a paper of great significance on hacking that threatens human life with the development of self-driving cars in the future.

Women and their rights: From totalitarianism to feminist empowerment in the context of the 20th anniversary of UN1325 on women, peace, and security between victims, leaders, and perpetrators

While analysing the heritage, input, and various implementation contexts of UNSCR 1325 on women, peace, and security, the authors investigate not only various approaches to women’s position and female empowerment in international engagement settings and cooperation models (UN, OSCE NATO, UNCDF), but also look at female empowerment in a global scope from women’s perspectives as victims, leaders, and perpetrators. By considering the need for complex engagement of international actors in stability, development, and crisis initiatives, the authors analyse NATO policy against sexual abuse and exploitation, on women’s financial inclusion, and the MenEngage initiative. While analysing the societal impact of radicalisation, they seek answers for effective reintegration and anti-radicalisation of female terrorists and foreign fighters. Authors examine the evolving gender equity and female empowerment policies of the EU in their foreign and domestic affairs, with particular interest in internal and external security standards for women’s safety.

IoT Security-Quality-Metrics Method and Its Conformity with Emerging Guidelines

This study proposes a security-quality-metrics method tailored for the Internet of things (IoT) and evaluates conformity of the proposed approach with pertinent cybersecurity regulations and guidelines for IoT. Cybersecurity incidents involving IoT devices have recently come to light; consequently, IoT security correspondence has become a necessity. The ISO 25000 series is used for software; however, the concept of security as a quality factor has not been applied to IoT devices. Because software vulnerabilities were not the device vendors’ responsibility as product liability, most vendors did not consider the security capability of IoT devices as part of their quality control. Furthermore, an appropriate IoT security-quality metric for vendors does not exist; instead, vendors have to set their security standards, which lack consistency and are difficult to justify by themselves. To address this problem, the authors propose a universal method for specifying IoT security-quality metrics on a globally accepted scale, inspired by the goal/question/metric (GQM) method. The method enables vendors to verify their products to conform to the requirements of existing baselines and certification programs and to help vendors to tailor their quality requirements to meet the given security requirements. The IoT users would also be able to use these metrics to verify the security quality of IoT devices.

Enterprise environment modeling for penetration testing on the OpenStack virtualization platform

The article presents the design of a model environment for penetration testing of an organization using virtualization. The need for this model was based on the constantly increasing requirements for the security of information systems, both in legal terms and in accordance with international security standards. The model was created based on a specific team from the unnamed company. The virtual working environment offered the same functions as the physical environment. The virtual working environment was created in OpenStack and tested with a Linux distribution Kali Linux. We demonstrated that the virtual environment is functional and its security testable. Virtualizing the work environment simplified the organization’s security testing, increased resource efficiency, and reduced the total cost of ownership of certain devices.

FOOD PRODUCTS PLACEMENT DURING COVID-19 PANDEMIC

The COVID-19 pandemic has entirely changed the patterns of behavior in the market. Physical distance, quarantine measures, safety and security protocols, and shortages along the entire supply chain, have particularly affected the placement of food products. In this regard, the paper aims to define the impact of various indicators on the placement of food products during the COVID-19 pandemic. An empirical study was conducted on a sample of 248 final consumers. The analysis was performed specifically for four groups of essential food products in a pandemic: meat and meat products, fruit and vegetables, milk and dairy products, cereals, and cereal products. The results showed a statistically significant impact of indicators such as safety and security standards, hygiene, price, product freshness, nutritional composition, chain transparency, and product quality. The impact of these indicators differs significantly depending on the analyzed product group. The obtained results indicate to the supply chain management which negative impacts need to be minimized to ensure timely placement of food products and more efficiently meet the needs of final consumers in a pandemic. Guidelines for future research are listed in the paper.

Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.

Correcting Errors in Color Image Encryption Algorithm Based on Fault Tolerance Technique

Security standards have been raised through modern multimedia communications technology, which allows for enormous progress in security. Modern multimedia communication technologies are concerned with fault tolerance technique and information security. As a primary method, there is widespread use of image encryption to protect image information security. Over the past few years, image encryption has paid more attention to combining DNA technologies in order to increase security. The objective here is to provide a new method for correcting color image encryption errors due to the uncertainty of DNA computing by using the fractional order hyperchaotic Lorenz system. To increase randomness, the proposed cryptosystem is applied to the three plain image channels: Red, Green, and Blue. Several methods were compared including the following: entropy, correlation, key sensitivity, key space, data loss attacks, speed computation, Number of Pixel changing rate (NPCR), and Unified Average Change Intensity randomness (UACI) tests. Consequently, the proposed scheme is very secure against a variety of cryptographic attacks.