1,721,174 research outputs found
Analysis of existing approaches to setting the intelligent management systems of transport undertakings
No full textThe problem of designing intelligent management systems (IMS) of dynamically variable objects (DO), operating under significant a priori uncertainty is considered. The analysis of existing approaches to developing DO IMS, methods, models and algorithms of their construction based on the integration of classical methods of management theory and artificial intelligence methods was presented. As examples of DO, rolling stock (TU) of the multi-mode enterprises is examined. The range of unresolved problems is identified, the purpose and objectives for the solution are formulated. Currently, the problem of designing the automatic management systems of dynamically variable objects is characterized by the transition from the paradigm of adaptive management to the paradigm of intelligent management. This is caused by a continuous complication of management objects and conditions of their operation, the emergence of new classes of computing means (in particular, distributed computing systems), high-performance telecommunications channels, and a sharp increase in the reliability and efficiency requirements for management processes under significant a priori and a posteriori uncertainty. Accounting of these factors is possible only on the basis of transition from "hard" algorithms of parametric and structural adaptation to the anthropomorphic principle of management formation
Enhancing copy-move forgery detection through a novel CNN architecture and comprehensive dataset analysis
No full textIn the contemporary digital era, images are omnipresent, serving as pivotal entities in conveying information, authenticating experiences, and substantiating facts. The ubiquity of image editing tools has precipitated a surge in image forgeries, notably through copy-move attacks where a portion of an image is copied and pasted within the same image to concoct deceptive narratives. This phenomenon is particularly perturbing considering the pivotal role images play in legal, journalistic, and scientific domains, necessitating robust forgery detection mechanisms to uphold image integrity and veracity. While advancements in Convolutional Neural Networks (CNN) have propelled copy-move forgery detection, existing methodologies grapple with limitations concerning the detection efficacy amidst complex manipulations and varied dataset characteristics. Additionally, a palpable void exists in comprehensively understanding and exploiting dataset heterogeneity to enhance detection capabilities. This heralds a pronounced exigency for innovative CNN architectures and nuanced understandings of dataset intricacies to augment detection capabilities, which has remained notably underexplored in the prevailing literature. Against this backdrop, our research broaches novel frontiers in copy-move forgery detection by introducing an innovative CNN architecture meticulously tailored to discern the subtlest manipulations, even amidst intricate image contexts. An extensive analysis of multiple datasets – MICC-F220, MICC-F600, and a combined variant – enables us to delineate a granular understanding of their attributes, thereby shedding unprecedented light on their influences on detection performance. Further, our research goes beyond mere detection, delving deep into comprehensive analyses of varied datasets and conducting additional experiments with differential training-validation sets and randomly labeled data to scrutinize the robustness and reliability of our model. We not only meticulously document and analyze our findings but also juxtapose them against extant models, offering an exhaustive comparative analysis
A prospective lightweight block cipher for green IT engineering
No full textThis chapter provides general requirements to modern block ciphers required for implementation at lightweight cryptographic transformations for critical distributed environment applications with Green IT conformance. It is given an overview of well-known block ciphers and lightweight primitives PRESENT and CLEFIA, defined at ISO/IEC 29192-2. It is given a specification of the lightweight block cipher Cypress that was recently developed and presented in Ukraine. Cypress does not use heavy computation operations, nor require any precomputed tables that allows efficient hardware implementation. The Cypress performance in software is approximately three times higher than AES one on Windows, Linux and Android platforms
Optimizing Merkle Proof Size Through Path Length Analysis: A Probabilistic Framework for Efficient Blockchain State Verification
No full textThis study addresses a critical challenge in modern blockchain systems: the excessive size of Merkle proofs in state verification, which significantly impacts scalability and efficiency. As highlighted by Ethereum’s founder, Vitalik Buterin, current Merkle Patricia Tries (MPTs) are highly inefficient for stateless clients, with worst-case proofs reaching approximately 300 MB. We present a comprehensive probabilistic analysis of path length distributions in MPTs to optimize proof size while maintaining security guarantees. Our novel mathematical model characterizes the distribution of path lengths in tries containing random blockchain addresses and validates it through extensive computational experiments. The findings reveal logarithmic scaling of average path lengths with respect to the number of addresses, with unprecedented precision in predicting structural properties across scales from 100 to 300 million addresses. The research demonstrates remarkable accuracy, with discrepancies between theoretical and experimental results not exceeding 0.01 across all tested scales. By identifying and verifying the right-skewed nature of path length distributions, we provide critical insights for optimizing Merkle proof generation and size reduction. Our practical implementation guidelines demonstrate potential proof size reductions of up to 70% through optimized path structuring and node layout. This work bridges the gap between theoretical computer science and practical blockchain engineering, offering immediate applications for blockchain client optimization and efficient state-proof generation
Trade-offs in Post-Quantum Cryptography: A Comparative Assessment of BIKE, HQC, and Classic McEliece
No full textThis study investigates the trade-offs inherent in three prominent post-quantum cryptographic algorithms: BIKE, HQC, and Classic McEliece. The evaluation of these algorithms was carried out across three different levels of security (L1, L3, and L5), centered on two crucial aspects: cryptographic size parameters and performance efficiency. Classic McEliece emerged as a space-demanding algorithm with significantly larger key sizes but managed to maintain relatively small ciphertext sizes. Conversely, HQC and BIKE presented smaller key and ciphertext sizes, indicating their potential suitability for applications with strict size constraints. In terms of computational costs, Classic McEliece required substantial resources for key generation, whereas HQC and BIKE exhibited balanced performance profiles. The findings underscore the importance of context-specific considerations when choosing an appropriate post-quantum cryptographic algorithm, highlighting the varying strengths and limitations of the analyzed algorithms
A Comprehensive Decentralized Digital Identity System: Blockchain, Artificial Intelligence, Fuzzy Extractors, and NFTs for Secure Identity Management
No full textExisting digital identification systems are often vulnerable to attacks as they are commonly based on authentication methods such as passwords, PIN codes, biometric data, etc., which can be easily forged or compromised. In this letter, we propose a digital identification system based on a unique set of user biometric data processed by Artificial Intelligence (AI) and fuzzy extractors to generate a cryptographically secure password linked to a unique Non-Fungible Token (NFT). Our system provides decentralized identification based on blockchain technology, which eliminates problems associated with centralized identification systems, such as cyber-attacks on central servers and data leaks. Our proposed system offers a higher level of user identification security by linking the user to their data through a unique NFT, generating a cryptographically secure password, and processing large volumes of biometric data using AI and fuzzy extractors. Our system provides a solution to many of these problems, making it important and relevant to many industries, including banking, medical, and financial sectors. The use of decentralized storage of information on the blockchain provides a high level of protection against hacking and reduces the likelihood of data breaches, making our system particularly relevant in the field of financial services and personal data protection
Lightweight stream ciphers for green IT engineering
No full textAt the moment the most widely used are symmetric cryptographic transformations, in particular, stream ciphers. The development of an efficient synchronous stream cipher is reduced to the construction of a pseudo-random sequence generator with defined cryptographic properties. It should be noted that in devices with limited computing power, low volume and low power consumption the implementation of reliable cryptographic methods is extremely complicated. Limited physical parameters, low power consumption, low computing power and other characteristic attributes of “green” IT engineering forces the use of new approaches for designing cryptographic protection tools. The main cryptographic transformations are considered and experimental studies of performance and statistical security are conducted. We propose new methods and hardware and software tools for lightweight stream encryption that meet the current requirements of “green” IT engineering. It is proposed synthesis method for the construction of nonlinear-feedback shift register, which allows creating nonlinear registers with design features that correspond to the certain predefined criteria
Security, Privacy, Confidentiality, and Trust in the Blockchain: From Theory to Applications
No full textFrom the financial and medical sectors to various supply chains, most industries have seen a sea change due to the blockchain [...
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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