In the current world of technology, various physical things can be used for facilitation of a human work. That is why the Internet of Things,an innovative technology and a good solution which allows the connection of the physical things with the digital world through the use of heterogeneous networks and communication technologies, is used. The Internet of Things in smart environments interacts with wireless sensor network (WSN) and mobile ad‐hoc network (MANET), making it even more attractive to the users and economically successful. Interaction between wireless sensor and mobile ad‐hoc networks with the Internet of Things allows the creation of a new MANET‐IoT systems and IT‐based networks. Such the system gives the greater mobility for a user and reduces deployment costs of the network. However, at the same time it opens new challenging issues in its networking aspects as well. In this work, the authors propose a routing solution for the Internet of Things system using a combination of MANET protocols and WSN routing principles. The presented results of solution's investigation provide an effective approach to efficient energy consumption in the global MANET‐IoT system. And that is a step forward to a reliable provision of services over global Future Internet infrastructure.

Layering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the quick and automated dispersal of assets across various digital wallets and exchanges. Machine learning emerges as a powerful tool for analyzing and identifying illicit transactions within Blockchain networks; however, a significant challenge remains in the form of a gap in advanced pattern recognition algorithms. This paper introduces a novel machine learning-based approach called Value-driven-Transactional tracking Analytics for Crypto compliance (VTAC) for the detection of illegal crypto transactions via Blockchain. The approach combines machine learning algorithms with a pre-training process, normalization, model training, and a de-anonymization process to analyze and identify illicit transactions effectively. Experimental evaluations show VTAC’s capability to detect illegal transactions with a 97.5% accuracy using the XG Boost model, outperforming existing methods with an accuracy of up to 95.9%. Key performance metrics, including precision, recall, and F1-score, consistently exceeded 95%, highlighting VTAC’s enhanced precision and reliability. The proposed solution will serve as an advisory framework to help financial crime investigators enhance the detection and reporting of suspicious cryptocurrency transactions in cyberspace.

Information systems of critical infrastructure provide services on which the core functions of a state and its economy depend as well as welfare of society. Such systems are becoming an increasingly common target for crimes and attacks in cyberspace, as their vulnerabilities can be exploited for malicious activities seeking financial or political gain. One of the main reasons that threatens the security of these systems is the weak control of remote access, otherwise defined as management of a system’s user identity. Management of user identity depends on user authentication, authorization and the assignment of certain rights in the digital space. This paper provides the proposed two-factor (2FA) digital authentication method for remote access to an information system of a critical infrastructure. Results of testing the method’s usability and resilience to cyber threats have shown that the system, in which the method was implemented, is protected from dangerous HTTP requests and publicly available system’s endpoints are protected from threatening inputs that could cause malicious activities on the critical infrastructure. Additionally, the implementation of the authentication API application ensures the rapidity of the method for less than 500 ms for 100 users working in parallel with the system at the same time.

Microservices are compact, independent services that work together with other microservices to support a single application function. Organizations may quickly deliver high-quality applications using the effective design pattern of the application function. Microservices allow for the alteration of one service in an application without affecting the other services. Containers and serverless functions, two cloud-native technologies, are frequently used to create microservices applications. A distributed, multi-component program has a number of advantages, but it also introduces new security risks that are not present in more conventional monolithic applications. The objective is to propose a method for access control that ensures the enhanced security of microservices. The proposed method was experimentally tested and validated in comparison to the centralized and decentralized architectures of the microservices. The obtained results showed that the proposed method enhanced the security of decentralized microservices by distributing the access control responsibility across multiple microservices within the external authentication and internal authorization processes. This allows for easy management of permissions between microservices and can help prevent unauthorized access to sensitive data and resources, as well as reduce the risk of attacks on microservices.

Quantum computing poses an unprecedented threat to classical cryptography, requiring new risk assessment paradigms. This paper proposes a Quantum-Adjusted Risk Score (QARS) model, a theoretical and methodological innovation within the EU’s PAREK framework (Post-quantum asset and algorithm inventory, risk assessment, road mapping, execution, key governance). QARS extends Mosca’s inequality—which defines a quantum threat timeline threshold—into a multi-factor risk scoring formula. We formalise QARS with mathematical expressions incorporating timeline, sensitivity, and exposure dimensions, each calibrated by factor weights and scaling functions. The design motivations for including these dimensions are discussed in depth. We present method for model calibration (including sector-specific weight adjustments) and outline validation strategies combining quantitative analysis and expert judgement. The proposed QARS model is situated in the context of the EU’s coordinated roadmap for post-quantum cryptography and cybersecurity regulations, illustrating how QARS supports compliance and strategic migration prioritisation. A prototype tool implementing QARS model is also provided to demonstrate practical applicability. Our contributions provide a unified approach to quantum risk assessment, marrying theoretical rigour with policy-relevant risk management needs to help organizations proactively address the quantum threat.

Confronting the challenges of securing communication in-vehicle wireless sensor networks demands innovative solutions, particularly as vehicles become more interconnected. This paper proposes a tailored communication security framework for in-vehicle wireless sensor networks, addressing both scientific and technical challenges through effective encryption methods. It segments the local vehicle network into independent subsystems communicating via encrypted and authenticated tunnels, enhancing automotive system safety and integrity. The authors introduce a process for periodic cryptographic key exchanges, ensuring secure communication and confidentiality in key generation without disclosing parameters. Additionally, an authentication technique utilizing the sender’s message authentication code secures communication tunnels, significantly advancing automotive cybersecurity and interconnectivity protection. Through a series of steps, including key generation, sending, and cryptographic key exchange, energy costs were investigated and compared with DTLS and TLS methods. For cryptographic security, testing against brute-force attacks and analysis of potential vulnerabilities in the AES-CBC 128 encryption algorithm, HMAC authentication, and HKDF key derivation function were carried out. Additionally, an evaluation of the memory resource consumption of the DTLS and TLS protocols was compared with the proposed solution. This work is crucial for mitigating risks associated with in-vehicle communication compromises within smart cities.