The advent of Internet of Things (IoT) ushers in a significant potential to integrate individuals, devices, and data, leading to a profound change in our professional and social environments. The small, resource-constrained IoT devices are usually deployed to collect various types of critical data in remote or unmonitored locations. Due to extensive interconnectivity, limited resources, and inadequate security design, IoT systems are vulnerable to communication-specific cyber threats, which aim to disrupt operations, steal sensitive information, or cause damage. To resolve the security concerns in IoT communications, many recent efforts have been devoted to designing authenticated key agreement protocols for IoT systems. However, not only most of the existing solutions fail to adopt cost-effective techniques for resource-limited IoT devices, but also they ignore the differentiation among various data types in the established session keys. A few approaches use traditional physical unclonable functions (PUFs) to address resource concerns, yet they introduce new security issues into IoT systems. Once the PUF cryptographic information is compromised by machine learning attacks, the entire authentication framework collapses. Therefore, in this paper we propose an authenticated key agreement protocol for device-to-gateway communication in IoT systems based on Chebyshev polynomial and probability-based PUF. We examine the proposed protocol’s security features through formal security validation. We also conduct performance evaluation through a simulation-oriented study, and the results clearly prove that the proposed protocol offers superior security and privacy, while maintaining low computational overhead.

This study employs network graph analysis to explore the social interactions and relationships between characters in Jane Austen’s Pride and Prejudice. By constructing a network graph where each node represents a character and edges denote the frequency and nature of their interactions, the analysis provides a quantitative and visual representation of the novel’s social structure. The resulting network graph reveals the centrality and influence of various characters, highlighting key relationships and social dynamics within the narrative. Through this approach, the study not only enhances our understanding of character relationships but also demonstrates the utility of network analysis in literary studies, offering new insights into the interpersonal complexities of Pride and Prejudice.

Fraud or Fake financial transactions seriously impact digital payment systems, necessitating more advanced detection mechanisms to mitigate the associated risks. Fraud trends that are always changing have made the traditional methods used to identify fraud cases obsolete, such as rule-based fraud detection and machine learning models. Recent studies have shown that Graph Neural Networks (GNNs) can better capture the relationship between financial transactions, while transformers are effective at recognizing sequential fraud patterns. Yet, the existing models that incorporate both do not perform well in this manner. To fill this gap in existing research, we have created a new model for detecting fraudulent transactions, the Hybrid GNN-Transformer Fraud Detection Model. This uses graphbased learning along with deep sequential feature extraction to better distinguish frauds from genuine transactions. The hybrid model had better performance compared to single models such as autoencoders, GNNs, and LSTMs, getting an accuracy of $99 \%$, as well as a precision of.99 and a recall of 1.00 when it comes to detecting fraudulent transactions. Comparisons show that GNNs and LSTMs still, when combined with transformers, there is an improved ability in the identification ofare able to capture key transaction interdependencies on their own. Still, when combined with transformers, they have an improved ability to identify complicated fraud activities.

Digital Content protection is important Ensuring document integrity in the digital age is a critical challenge. Traditional visible and invisible watermarking methods offer partial protection but have limitations. Visible watermarking can be removed and degrade quality, while invisible watermarking requires specialized tools and may degrade through compression. TThis study presents an innovative document authentication framework that leverages part-of-speech (POS) tag-based graph representations. By transforming textual data into structured graphs, the method captures the unique syntactic signature of each document. This linguistic fingerprint is then encrypted and stored independently, enabling robust verification of document authenticity. The approach not only reinforces security and integrity but also seamlessly integrates with existing natural language processing (NLP) pipelines.

Credit card fraud detection is a critical challenge in modern financial systems, requiring robust and efficient solutions to identify suspicious transactions accurately. This study addresses the issue by utilizing machine learning techniques to detect potentially fraudulent transactions. A key focus of the research is the handling of imbalanced datasets, where genuine transactions vastly outnumber fraudulent ones. To address this imbalance, we increased the sample size of fraudulent data using oversampling techniques and subsequently applied four distinct machine learning models to assess their performance. Through iterative experimentation, we identified the optimal magnification ratio that enhances the model’s ability to distinguish between legitimate and fraudulent transactions. The results demonstrate that balancing the dataset significantly improves detection accuracy, providing insights into effective model configurations for realworld applications. This research contributes to the development of more reliable and efficient fraud detection systems in the financial sector.

This article presents a quantitative study on the performance of an asynchronous chain model [ 31 ] during its theoretical design stage. The asynchronous chain [ 31 ] in this study is asynchronous along with adaptively sized blocks in a proactive manner, whereas the conventional chain controls the block posting in a strictly synchronous manner to the fixed-sized blocks. The model of the adaptive chain, with a rather “reactively” dynamic size of blocks as shown in [ 30 ], can be compared with the proposed model, which is asynchronous with a “proactively” dynamic size of the blocks. It is assumed in this article that Variable Bulk Arrivals (VBA) of transactions in the Poisson distribution and Asynchronous Bulk Posting (ABS) of transactions off a block potentially in different capacity in exponential time, referred to as VBAABS. Basic numerical simulations results have been reported in [ 31 ] primarily for feasibility validation purpose. In our earlier conference version [ 31 ], we have presented the work with the focus on development and validation of the performance model in a quantitative manner with extensive numerical simulations to demonstrate the efficacy of the proposed asynchronous chain model, and in this article, we extend the work to also include substantially new works such as an extensive comparative study in performance versus other blockchain models such as the baseline chain model [ 29 ] (see Comparison between the Asynchronous Chain Model and the Baseline Chain Model) and the adaptive chain model [ 30 ] (see Comparison between the Asynchronous Chain Model and the Adaptive Chain Model under Various Network Traffic). Then, a summary, by each chain model in consideration, is provided for clarity (see Chain Model Insights). Furthermore, a new simulation is conducted with a focus on the performance of microtransactions as a type of transactions to be commonly expected in the gaming decentralized applications to demonstrate the benefit from the proactive asynchrony of block postings (see Simulation and Analysis of the Impact of the Asynchronous Chain Model on the Performance of Microtransactions). Last, the implementation results and analysis are shown based on the Ethereum open source as reported in [ 31 ].

The Internet of Drones (IoD) has latterly started to gear up its applications in diverse sectors of the society as a result of high adaptability and adjustability to new circumstances. Security, privacy, and storage issues still remain as major barriers for next-generation IoD systems to meet their general applicability requirements, even though many one-keyfor-all static authentication and append-only blockchain assisted systems have been proposed by the IoD community. First, bearing channel bandwidth and drones resource constraints in mind, authentication protocols with less computation and communication overhead are preferable. Second, the IoD drones might collect different types of data simultaneously, a unique secret session key for each type of data is needed to prevent data leakage from unauthorized parties. Third, the permanent storage of each drones cryptographic and task information on the appendonly blockchain raises significantly alert after a long period of operation and/or an exponential growth of drones. Motivated by the research challenges presented above, we propose a redactable blockchain-assisted application-aware authentication system, also referred to as ReBAS, for next-generation IoD applications, where the drones shuttle back and forth between different flying zones to collect diverse types of data. The Chebyshev polynomial, redactable consortium blockchain, and chameleon hash function are adopted to significantly minimize the computational, communication, and storage overheads of cryptography-related operations. According to the security verification, and formal and informal security analysis, the ReBAS not only guarantees secure and dynamic authenticated key establishment, but also is in compliance with the security requirements of Canetti-Krawczyk adversarial framework. We also develop a rigorous simulation framework and conduct an extensive comparative study. The experimental results demonstrate that the ReBAS can minimize the overheads in computation, communication, and storage while enhancing scalability.

This study explores quantum computing and blockchain, focusing on quantum-safe algorithms. As quantum computing progresses, it threatens blockchain cryptography, necessitating quantum-resistant/safe algorithms. Using statistical analysis and simulations to address future cyber threats, we analyze quantum-safe cryptography in blockchains. We pioneer machine learning models to assess quantum-safe algorithm performance across encryption, key generation, signatures, speed, scalability, energy efficiency, and security. Our analysis, referencing Ethereum’s Ether values, pinpoints areas for quantum-safe cryptography improvements, addressing limitations and proposing solutions to strengthen security and efficiency. In experiments, we observed a 15% increase in encryption strength, 10% enhanced key generation efficiency, and improved reliability parameters. These findings stress the importance of machine learning in quantum-safe cryptography for widespread adoption and long-term security against quantum threats. In conclusion, our research paves the way for the integration of quantum-safe algorithms, ensuring the resilience of blockchain systems in the face of quantum advancements.

Accurate and automated sound classification enables a strong groundwork for diverse advanced deep learning applications within the audio and music domain. This study focuses on the application of Convolutional Neural Networks (CNN) and combined LSTM (Long Short-Term Memory) and GRU (Gated Recurrent unit) models for instrument classification from audio signals, contributing to intelligent audio processing systems. Our proposed model exclusively utilizes the Mel-frequency cepstral coefficients (MFCCs) extraction from the audio data for preprocessing. A large and complex dataset, including Nineteen instrument classes are used for training and evaluation. These experimental results demonstrate promising performance, with our proposed CNN architecture achieving an impressive accuracy of 97%, and the LSTM-GRU model achieves a lower accuracy of 80%, compared to the CNN model on the multi-label sound classification task for instruments classes, but its ability to model temporal dependencies add valuable insights into the dynamics of instrument audio sequences. These findings provide valuable insights for researchers and practitioners in audio signal processing and machine learning.

In the realm of Decentralized Finance (DeFi), this manuscript introduces a Hybrid Cross-Chain Model. As DeFi architectures grapple with the complexities of monolithic single-chain platforms, our proposed model orchestrates a symphony of multiple chains to facilitate seamless cross-chain communication, offering a poised solution to scalability and transaction speed challenges. Incorporating modeling effects and simulations, our rigorous performance evaluation underscores the model's excellence and includes an in-depth analysis of its performance, particularly focusing on robust security measures. The model is positioned as a cornerstone in an interconnected DeFi landscape by emphasizing stringent measures to ensure data integrity and uphold consensus mechanisms. User-centric enhancements promise swift transaction confirmations and reduced fees, improving the overall experience. The abstract culminates with a comparative analysis, positioning the Hybrid Cross-Chain Model as an innovative solution with profound implications for the future of DeFi. This manuscript advocates for ongoing research and development, heralding a new era of sophistication and resilience in decentralized finance.

This research presents a sophisticated technological framework for Cross-Chain Decentralized Finance (DeFi) and Smart Contract systems by seamlessly integrating Markov Models, Brownian Motion, and Stationary Processes. Focused on enhancing the adaptability and efficiency of financial interactions across interconnected blockchain networks, this framework establishes the foundational elements necessary for dynamic system modeling. The incorporation of Markov Models captures state transitions, Brownian Motion models random fluctuations, and Stationary Processes ensure statistical stability. The paper explores the technological implications of these stochastic processes, addressing challenges in system interoperability, latency, and security within decentralized financial ecosystems. Envisioning a future where decentralized systems are optimized and resilient, the research investigates advancements in blockchain protocol design, consensus mechanisms, and transaction validation strategies. The proposed framework, influenced by the dynamic and statistical nature of Brownian Motion and Stationary Processes, underscores the need for robust data structures, real-time data feeds, and decentralized oracle networks. This research invites collaboration from the blockchain, smart contract, and stochastic modeling communities to contribute to the ongoing exploration and refinement of this powerful technological framework, poised to reshape the landscape of cross-chain financial technologies.

This research proposes the methodology to extract common configuration parameter set by applying multiple benchmark applications including TeraSort., TestDFSIO, and MrBench on the Hadoop Distributed File System. In the process of determining parameter set for each stage, one parameter and its associated values selected which is reduced system performance in terms of overall execution time difference are measured by multiple applications on a Hadoop cluster. The experimental results demonstrate the proposed extended greedy manner provide a feasible benchmark model for the multiple tasks. In this way, we have found several parameter value sets that can reduce the execution time by 27% of the values provided by Hadoop default.