Reviewerly/exHarmony huggingface dataset:
Collection and Statistical Analysis of a Fixed-Text Keystroke Dynamics Authentication Data Set

Keystroke dynamics authentication is a promising method of improving account security with minimal detriment for user convenience. While there is an abundance of research, there is a lack of available data sets. In this study, data sets for keystroke dynamics authentication were collected for a set of 6 passwords from a group of participants, and a correlation algorithm was developed to analyze and use these data sets for authentication. The experiments aim to produce data for keystroke dynamics authentication benchmarking, and to show the effect of typing speed and consistency, password length and entropy on prediction accuracy. Through simple correlation methods, the authors achieve an Equal Error Rate varying between a range of 2.57% and 29.7%. These result give insight into what may cause the accuracy to vary depending on the person and the password.
A Tree-Mapped Taxonomy of Blockchain Attacks

Blockchains provide decentralized data storage and have gained popularity over the past decade. Their popularity has led to a multitude of attacks in research and practice. We analyzed the available literature and found 147 distinct blockchain attacks. We examined these blockchain attack vectors relative to existing blockchain attack taxonomies and found that current taxonomies miss one-third (33.33%) of attack types. We found that blockchain attacks are evolving faster than we can classify them according to conventional taxonomies. To address this, we provide a novel taxonomy of blockchain attacks using a treemap visualization. We believe this taxonomy will help the community understand the context for new attack discoveries and could help researchers better forecast opportunities for future work in blockchain security.
NESec: A Modified-UI Honeyword Generation Strategy for Mitigating Targeted Guessing Attacks

By harnessing Graphics Processing Unit (GPU), Field-programmable Gate Array (FPGA), and advanced cracking techniques, the success rates of server-side threats on passwords have reached unprecedented levels. Honeywords, also known as decoy passwords, have emerged as a promising detection strategy against this threat scenario. However, existing work falls short in creating a trap to counter targeted guessing attackers <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{TgA})$</tex> who exploit users' personal information to bypass honeyword-based traps. In this paper, we introduce two fundamental honeyword generation modules, namely NSecure-modifiedUI and ESecure-modifiedUI. Building upon these primary modules, we propose a hybrid honeyword-based strategy named NESec, which significantly enhances the ability to detect <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{TgA}$</tex> 's activities. A comparative analysis showcases the usability advantages and security benefits of the proposed NESec approach.
Multi-Context Modeling of Processes and Services in Cyber-Physical Educational Space

The development of cyber-physical systems in various areas of modern life is a labor-intensive and expensive process, which determines the need for preliminary modeling of key processes and scenarios, which takes into account the dynamic change of context information. The article presents an approach for multi-context modeling in a cyber-physical and social educational school space.
Exploring Federated Learning for Data Integration: A Structured Literature Review

Data integration is utilized to integrate heterogeneous data from multiple sources, representing a crucial step to improve information value in data analysis and mining. Incorporating machine and deep learning into data integration has proven beneficial, particularly for messy data. Yet, a significant challenge is the scarcity of training data, impeding the development of robust models. Federated learning has emerged as a promising solution to address the challenge of limited training data in various research domains. Through collaborative model training, robust model training is enabled while upholding data privacy and security. This paper explores the potential of applying federated learning to data integration through a structured literature review, offering insights into the current state-of-the-art and future directions in this interdisciplinary field.
A New Approach for Crypto Off-loading Based on Personal HSM

Crypto off-loading aims at computing cryptographic procedures in dedicated hardware in order to increase performances or to provide more security and trust. For example cloud providers offer crypto off-loading services realized in hardware secure module (HSM), typically for SSL/TLS asymmetric operations required for HTTP server authentication. We recently introduced the concept of personal HSM devices, based on secure elements grids. In this paper we present a new approach based on these devices, which relies on two features. First a direct TLS session with secure element enables a simple and secure key management. Second performance elasticity is made possible by using multiple secure elements. We present experimental results for a personal HSM equipped with ten secure elements.
Application of UML Descriptive Modeling Techniques in Model Driven Engineering of Information Security Systems

To be competitive, today's organizations must protect their corporate information. Directives, regulations, and legal provisions, information security standards such as ISO, as well as sector-specific regulations and best practices such as SOX, HIPAA, and PCI are commonly used to protect information. Usually, however, this approach is not sufficient, and therefore a complex approach is needed, which only an information security system (ISS), specially designed for the relevant activity can provide. For this purpose, we present a method for designing ISS using Model-driven engineering and the use of the object-oriented language UML. We show how it can be used to represent models, both their architecture and their dynamic behavior. We also show how model transformation can be implemented using UML.
Using LLMs in Cyber-Physical Systems for Agriculture - ZEMELA

This paper presents the idea of developing an advisory service using the capabilities of generative artificial intelligence and in particular of Large Language Model. The service will assess the risks for farmers when preparing projects under different programs, taking into account the Bulgarian legislation related to agriculture, as well as the requirements of the relevant program. The results of a feasibility analysis are summarized in the article. Furthermore, two architectural approaches are discussed. The service will be integrated in the platform for smart agriculture named ZEMELA. A brief overview of this platform is also given in the article.
Privacy-Preserving Attribute-Based Access Control with Non-Monotonic Access Structure

Attribute-Based Encryption (ABE) with non-monotonic access policies provides fine-grained access control for widespread applications like Cloud-assisted HealthIoT systems. In this context, multi-authority ABE with untrusted authorities eliminates the need for a trusted authority, but ensuring user's identity and attributes-set privacy against these authorities remains a significant challenge. This paper proposes a new, efficient multi-authority ABE approach that preserves user's identity privacy and attributes-set privacy, and is secure against collusion attack. Also, the proposed approach provides non-monotonic access policies, which supports positive and negative constraints using NOT operation as well as AND and OR operations.
Vulnerability Assessment Based on Real World Exploitability for Prioritizing Patch Applications

With the increasing understanding of attackers regarding the characteristics of control systems and the growing connectivity with information technology, security incidents targeting control systems are on the rise. The number of vulnerabilities related to these incidents is increasing yearly, making applying timely patches for all vulnerabilities impossible. The current common vulnerability assessment framework, considered the basis for vulnerability patching, has limitations in that it does not consider weaponization after the vulnerability discovery and does not adequately reflect the exploitability in real-world environments. Therefore, this study propose an evaluation method that considers both dynamic and static characteristics of vulnerabilities in control systems, based on publicly available data. To achieve this, we define criteria for classifying the weaponization level of exploit code utilized by attackers and introduce a new element that incorporates this classification, improving the conventional vulnerability scoring system metrics. By applying this evaluation approach, we can identify vulnerabilities in control systems likely to be exploited in real-world scenarios, enabling prioritized patching and a proactive defense against advanced persistent threat attacks.
BETA-FL: Blockchain-Event Triggered Asynchronous Federated Learning in Supply Chains

BETA-FL provides a distributed federated learning framework implemented for supply chains by tightly integrating private- permissioned blockchain for the trusted alliance among various actors involved in the supply chain. With a trusted ledger as the moderator in federated learning workflow, our approach ensures protection against malicious backdoor attacks on performance from both server and clients. Additionally, our asynchronous training regime allows scalability to a large number of federated clients with small and constant delay caused due to an event-triggering scheme. We showcase a classification task on spectrogram data as a potential use-case in the food supply chain to avoid food wastage. Finally, we facilitate a dedicated channel for regulatory bodies in our blockchain environment for inspections and audits pertaining to the functioning of the supply chain.
Securing Federated Learning against FGSM Attacks with Adaptive Trust Scores and Blockchain Updates

Federated learning (FL) is a distributed Machine Learning (ML) approach that allows multiple clients to train a global model collaboratively while maintaining their data decentralized. However, this learning paradigm is prone to various security threats, including Fast Gradient Sign Method (FGSM) attacks. This paper proposes a novel method for enhancing the security and privacy of FL by reducing the risk of FGSM attacks through a Trust Score-based (TS) approach and blockchain technology. The TS is determined based on the client's average loss and variance, and only clients with a high TS are allowed to participate in the model averaging process. This method significantly improves the security and accuracy of the global model, preventing security breaches in FL while maintaining the privacy of client data and model parameters. The effectiveness of the proposed method is evidenced by the experimental results obtained from two distinct datasets. Compared to the attack without the proposed method, the proposed method improved the global accuracy by an average of 10% while reducing the success rate of FGSM attacks by an average of 87%. The incorporation of blockchain technology enables the creation of a decentralized network, which enhances the privacy and security of the FL process.
Blockchain-Based Data Sharing Platform Customization with On/Off-chain Data Balancing

Blockchain is widely considered as a promising solution, which can build a secure and efficient environment for data sharing. With more and more people working remotely and privacy becoming a major concern, having a secure and efficient way of sharing data has become a necessity. Blockchain being a decentralized ledger emphasizing cryptography obviously helps with data security and privacy, but the technology can suffer from major constraints in the context of data sharing such as on-chain data volume, storage, network performance, off-chain security, and more. In this study, we are looking at customization as a dynamic or adaptive strategy to determine when/how/what data should be stored on-chain versus off-chain to face the trade-off between performance and security; we explore the relevant research questions and the metrics by implementing a proof-of-concept solution using Hyperledger-fabric and IPFS (Inter Planetary File System). The results show that the on-chain latency increases with rising on-chain data ratio, whereas the off-chain exhibits reduced latency with respect to the on-chain ratio. In conclusion, the balance between on-chain and off-chain data storage in blockchain networks is a nuanced decision that hinges on the nature of the data, resource availability, and the desired trade-off between security and performance. A customized approach, where sensitive data is securely stored on-chain, while other data is managed off-chain for improved throughput, can help achieve the optimal equilibrium, ensuring both data integrity and network efficiency.
ParaSpell XCM SDK: A New Protocol for Interoperability in Polkadot Paraverse

Interoperability is a critical aspect of blockchain technology, allowing different blockchain networks to communicate and exchange information with each other. However, achieving interoperability can be challenging due to the decentralized and heterogeneous nature of blockchains. In this paper, we propose a modular solution called ParaSpell that groups XCM-compatible Parachains in the Polkadot ecosystem and significantly simplifies the process required to send a cross-chain message. The solution is based on the XCMP protocol, which is a protocol that uses collators as maintainers and allows communication with sovereign blockchains. We discuss the design of our solution and test it in depth. We show that the solution can work reliably in localhost, testnet, and mainnet application cases. The testing results demonstrate that the ParaSpell solution outperforms the native PolkadotJS UI in terms of transaction execution time, with an average of 48.4 seconds compared to 95.4 seconds for the native UI. This is a significant improvement of almost 50%, which highlights the effectiveness of the proposed solution. Furthermore, all the source codes are available on GitHub, and we also provide a demo application.
Standardizing Personal Medical Data in 5G and 6G Era

Electronic health records (EHR) are crucial for facilitating comprehensive and efficient patient care, enabling seamless information sharing among healthcare providers and enhancing clinical decision-making. Growing use of smart wearables and home automation devices with embedded sensors has created numerous opportunities for developing novel high-quality solutions in many sectors, including eHealth. The potential of using user-collected health data is enormous in the 5G and 6G era, making it a paradigm that holds promise for improving people's health and wellbeing. A step closer to ensuring continuous healthcare access would be achieved if EHRs were equipped to store and interpret data collected by sensors. This would result in standard-compliant personalized medical services. This paper presents the process of standardizing the data by defining semantic constraints for healthcare data types and modeling a procedure for validating the obtained data ensuring compliance to relevant standards and regulations.
Psychological Information Sharing Using Ethereum Blockchain and Smart Contracts

Blockchain technology has increasingly impacted our society across all industries over the last decade. Aside from being one of the most widely used technologies, blockchain not only enables cryptocurrencies with anonymous properties that apply robust information security for consolidating and building trust between users and providers but also provisions a new channel for information sharing. Further, deploying blockchain can in-crease security, privacy, and reliability for data sharing at a low cost. Hence, this paper presents a solution framed by considering the anonymous properties of blockchain with the idea of building a secure and trustworthy platform for sharing personal information. The platform's primary goal is to create a community where information such as psychological data from various stakeholders could be used in related exploratory research. Because of the nature of blockchain, imparting privacy over personal data will not necessarily violate any current ethics with blockchain technology. The architectural design shows the development process's means and the implementation of a unique psychological information-sharing platform. The proposed solution could encourage more people to share their data through blockchain and make it accessible to a broader range of users.
A Lite-SVM Based Semantic Search Model for Bigdata Analytics in Smart Cities

The distributed file system used by conventional concepts of big data lacks the extra security and privacy features, which are required more time and energy wastage. In this research, propose a methodology for managing private and safe big data modeling with LITE SVM-based semantic application. It's not possible to scale the big data platform, and there's no assurance of security and time scaling. The suggested technique uses an SVM machine to accurately address these concerns without compromising either security or privacy and without affecting the energy consumption of the application. The main objective of this research is, to manage the Bigdata platform with effective storage in less Time of Conversion (ToC). The existing models like Genetic Algorithm (GA), Differential Annealing (DE) for global optimization, and Fuzzy models were limited to functioning. So that to crossover the above limitations proposed Lite SVM machine learning techniques. The accuracy 82.23%, true positive rate 98.23%, recall 84.53%, & F1 score 92.34% were observed with suggested Lite SVM and compared with past techniques. Such new and enhanced SVM method outcomes are competitive with the most recent methods.
An Adaptive Blockchain-Based Decentralized Network Computing and Performance Analysis

This paper presetns an adaptive chain as one of the most trustworthy blockchain-based network solutions for security and privacy for industrial applications and a Variable Bulk Arrival and Variable Bulk Service (VBAVBS) model in order to provide a quantitative method to support demonstration of the validity, feasibility, and further the potential performance benefits of the adaptive chain. The model assumes variable bulk arrivals of transactions in Poisson distribution, i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M^{1,n}$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> represents the number of slots across all the mined transactions, and variable bulk services of transactions, each of which applies to a block potentially of different capacity in terms of the number of slots in it, in exponential time, i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M^{1,i,n}$</tex> , for posting in the current block, namely, VBAVBS. The difference between VBASBS [22] and VBAVBS is that in VBASBS, the state <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{n}$</tex> is the only state to transition back into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{0}$</tex> while in VBAVBS, every state <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{i},0 &lt; i\leq n$</tex> , potentially transitions back into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{0}$</tex> . VBAVBS will reveal the performance advantages of the adaptive chain versus the baseline chain, i.e., VBASBS, with respect to the average time for a slot to stay (or wait) in the block and the average spatial requirement by the slots in the block. Numerical simulations are conducted on Matlab to compute the models and a comparative study will be demonstrated on VBAVBS versus VBASBS.
Unveiling Ethereum's Hidden Centralization Incentives: Does Connectivity Impact Performance?

Modern public blockchains like Ethereum rely on p2p networks to run distributed and censorship-resistant applications. With its wide adoption, it operates as a highly critical public ledger. On its transition to become more scalable and sustainable, shifting to PoS without sacrificing the security and resilience of PoW,Ethereum offers a range of consensus clients to participate in the network. In this paper, we present a methodology to measure the performance of the consensus clients based on the latency to receive messages from the p2p network. The paper includes a study that identifies the incentives and limitations that the network experiences, presenting insights about the latency impact derived from running the software in different locations.
Automation of Blockchain Network Setup in Offering Blockchain as a Service (BaaS)

Blockchain is an emerging technology which is eliminating the need for central entity and enabling trust through software technologies in digital platforms. Blockchain Technology has potential in Government, Business and Societal applications. Challenges faced by the developers in developing Blockchain applications due to its complex nature are addressed by various Blockchain as a Service solutions. In this paper, National Blockchain Framework enabled BaaS, a framework supporting permissioned Blockchain platforms, is presented which aids in rapid end-to-end application development including quick network setup, deployment of smart contracts, user enrollment and exposing Gateway APIs to the applications developers. This framework also supports in augmenting the Blockchain network with the configurable Interplanetary File System for distributed content addressable file storage. Further it provides APIs for easy integration of 3rd party applications which are of National Importance.
A Computer Aided Design Tool for NISQ Logic Synthesis

Computed Aided Design (CAD) is essential to synthesize noisy intermediate scale quantum (NISQ) information and communication systems. The prevailing model for NISQ systems is gate-based and uses logical components arranged into a circuit to represent different quantum operations. A CAD Tool for Quantum Logic is proposed to synthesize gate-based NISQ circuits for computing, communication, or sensing by supplying designers with parameterized versions of common quantum algorithms as well as a design space for simulating photonics hardware implementations. Available in a software package called the CADTQL, designers can test their conventional and optical quantum logic configurations and realize more efficient designs.
Web3 and Blockchain for Modernizing the Reseller Market

This paper presents a proof-of-concept (PoC) that leverages Web3 technology, specifically blockchain and Non-Fungible Tokens (NFTs), to create a secure, transparent, and fair reseller market without the need for a trusted third party. The PoC demonstrates that NFTs can be used to authenticate items, build trust between parties and establish verifiable product own-ership. By paralleling NFT transfers with the sale of items, the practicality of selling counterfeit goods is reduced. Furthermore, the blockchain-based storage of product information enables verification of origin, history, and authenticity by any involved party, fostering transparency and equal access to information for buyers and sellers. We demonstrate that Web3 technology can effectively address critical issues in traditional reseller markets, offering substantial benefits to all parties involved.