Security Parameters Research Articles

An Improved and Secured 3-Factor Authentication Scheme for WSN in IoT Based Applications

Wireless Sensor Networks (WSN) has top-notch capacity for numerous domains of utility due to the potential to sense and apprehend unattended environments. IoT is playing an increasingly more essential position in clever healthcare, smart transportation and elegant grid using WSN. Therefore, challenges like privacy and data security associated with IoT wireless sensor networks because of sensor node being captured easily, consume less computing energy and low storage. Designing a secure authentication scheme will enable secure communication in WSN. Here, we proposed a improved three-factor authentication scheme for WSN utilizing biometric, password and smart card withstand against security attacks. Subsequently, by comparing the security, functional and performance parameters of our proposed scheme to the other schemes, our scheme provides better and efficient one. Therefore, our scheme is fairly workout in real time IoT applications involved in WSN scenario.

Research on Collaborative Defense Method of Hospital Network Cloud based on End-to-end Edge Computing

This research introduces a groundbreaking collaborative defense mechanism that utilizes end-to-end edge computing to bolster the security of decentralized hospital cloud systems. By integrating intrusion detection systems, firewalls, anomaly detection, and threat intelligence in a unified manner through the efficiency of edge computing, this approach marks a significant advancement in healthcare cybersecurity. Through rigorous testing with a substantial dataset, the system demonstrated exceptional performance metrics, including a remarkable 95% accuracy in threat detection, a low false positive rate, and a swift response time of merely 0.25 seconds. Notably, the system effectively mitigates computational overhead, thereby optimizing resource utilization. Comparative analysis with existing methodologies underscores the superiority of this novel framework, particularly in terms of geolocation accuracy, the minimization of false positives, and expedited reaction capabilities. This study’s collaborative defense strategy, underpinned by end-to-end edge computing, presents a holistic and innovative solution to the escalating cyber threats facing healthcare infrastructures. By redefining the parameters of security in medical settings, it paves the way for a safer and more resilient healthcare information technology ecosystem.

A novel secure privacy-preserving data sharing model with deep-based key generation on the blockchain network in the cloud

Cloud computing is currently emerging as a developing technology in which a Cloud Service Provider (CSP) is a third-party organization that provides effective storage of data and facilities to a large client base. Saving information in a cloud offers users the satisfaction of accessing it without the need for direct knowledge of the distribution and management of an infrastructure. The primary objective is to develop a novel, secure, and privacy-preserving data-sharing model that utilizes deep-based key generation on blockchain in the cloud. Data communication is done using multiple entities. The research aims to develop a collaborative data-sharing method in the cloud for the authentication scheme for cloud security on blockchain and smart contracts. Initialization, registration, key generation, authentication of data sharing, and validation are carried out here. The proposed data-sharing model involves a revenue distribution model that depends on Multiple Services (MS) models to improve multiple cloud services. The security parameters namely passwords, hashing functions, key interpolation, and encryption are used for preserving the Data privacy and here the SpinalNet is used for generating keys. Furthermore, the devised SpinalNet_Genkey obtained a value of 45.001 MB, 0.002, and 0.003 sec for memory usage, revenue, and computation cost.

Efficient Arbitrated Quantum Digital Signature with Multi‐Receiver Verification

AbstractQuantum digital signature is used to authenticate the identity of the signer with information theoretical security while providing non‐forgery and non‐repudiation services. In traditional multi‐receiver quantum digital signature schemes without an arbitrator, the transferability of one‐to‐one signature is always required to achieve unforgeability, with complicated implementation and heavy key consumption. In this article, an arbitrated quantum digital signature scheme is proposed, in which the signature can be verified by multiple receivers simultaneously, and meanwhile, the transferability of the signature is still kept. This scheme can be simplified performed to various quantum secure networks, due to the proposed efficient signature calculation procedure with low secure key consumption and low computation complexity, by employing one‐time universal hashing algorithm and a one‐time pad encryption scheme. The evaluation results show that this scheme uses at least two orders of magnitude less key than existing signature schemes with transferability when signing files of the same length with the same number of receivers and security parameter settings.

FINANCIAL AND ECONOMIC SECURITY AND DEVELOPMENT OF PRIORITY SECTORS OF THE NATIONAL ECONOMY UKRAINE: CASUAL NEXUS EMPIRICAL

The paper aims to investigate the causal nexus of the parameters of financial and economic security and the development of priority sectors of the national economy (on the example of agriculture, industry, construction, and transport in Ukraine) in the years 2010-2021. The results of the causal analysis showed the greatest degree of variability in the development of Ukrainian industry from the production, economic, credit and debt components of its financial security; agriculture, forestry, and fisheries – the security of money circulation and the credit component of financial security; construction, transport, warehousing, postal and courier – all components of financial security, except investment. The practical significance of the research results lies in the development of tools for modelling the impact of policy measures of strengthening financial and economic security on the development of priority sectors of the national economy, which allows for predicting the consequences of the implementation of the state security policy in the field of structural and institutional changes.

Predictive digital twin driven trust model for cloud service providers with Fuzzy inferred trust score calculation

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection.

Representations of group actions and their applications in cryptography

Cryptographic group actions provide a flexible framework that allows the instantiation of several primitives, ranging from key exchange protocols to PRFs and digital signatures. The security of such constructions is based on the intractability of some computational problems. For example, given the group action (G,X,⋆), the weak unpredictability assumption (Alamati et al. (2020) [1]) requires that, given random xi's in X, no probabilistic polynomial time algorithm can compute, on input {(xi,g⋆xi)}i=1,…,Q and y, the set element g⋆y.In this work, we study such assumptions, aided by the definition of group action representations and a new metric, the q-linear dimension, that estimates the “linearity” of a group action, or in other words, how much it is far from being linear. We show that under some hypotheses on the group action representation, and if the q-linear dimension is polynomial in the security parameter, then the weak unpredictability and other related assumptions cannot hold. This technique is applied to some actions from cryptography, like the ones arising from the equivalence of linear codes, as a result, we obtain the impossibility of using such actions for the instantiation of certain primitives.As an additional result, some bounds on the q-linear dimension are given for classical groups, such as Sn, GL(Fn) and the cyclic group Zn acting on itself.

Subgraph-level federated graph neural network for privacy-preserving recommendation with meta-learning

Graph neural networks (GNN) are widely used in recommendation systems, but traditional centralized methods raise privacy concerns. To address this, we introduce a federated framework for privacy-preserving GNN-based recommendations. This framework allows distributed training of GNN models using local user data. Each client trains a GNN using its own user–item graph and uploads gradients to a central server for aggregation. To overcome limited data, we propose expanding local graphs using Software Guard Extension (SGX) and Local Differential Privacy (LDP). SGX computes node intersections for subgraph exchange and expansion, while local differential privacy ensures privacy. Additionally, we introduce a personalized approach with Prototype Networks (PN) and Model-Agnostic Meta-Learning (MAML) to handle data heterogeneity. This enhances the encoding abilities of the federated meta-learner, enabling precise fine-tuning and quick adaptation to diverse client graph data. We leverage SGX and local differential privacy for secure parameter sharing and defense against malicious servers. Comprehensive experiments across six datasets demonstrate our method’s superiority over centralized GNN-based recommendations, while preserving user privacy.

Long-Short Term Memory Network Based Model for Reverse Brute Force Attack Detection

Reverse brute force attacks pose a significant threat to the security of online systems, where adversaries attempt to gain unauthorized access by systematically testing a multitude of username and password combinations against a single account. To address this challenge, the research presents an innovative Long-Short Term Memory Network based model designed to detect such attacks. The model utilizes LSTM algorithms to analyze login attempt patterns, identifying anomalies that may indicate reverse brute force attacks. By examining various factors like user login behavior, IP address, and time-based patterns, the model distinguishes legitimate access attempts from potential attacks with high accuracy. It incorporates real-time threat intelligence feeds and historical data analysis to continuously adapt and improve its detection capabilities. The model dynamically adjusts security parameters, enforces account lockouts, and communicates with firewall systems to block suspicious IP addresses, thus providing a proactive response to thwart attacks. The research evaluates the effectiveness of the AI model through simulated and real-world testing scenarios, demonstrating a significant reduction in false positives and successful prevention of reverse brute force attacks. Overall, the developed AI model offers a sophisticated and proactive solution to the evolving threat of reverse brute force attacks, contributing to the advancement of cybersecurity measures.

Harris Hawk optimization driven adaptive image encryption integrating Hilbert vibrational decomposition and chaos

Image encryption is a technique used to protect the confidentiality of digital images. Existing encryption algorithms use chaotic maps for generating random sequences. These sequences purely depend on the fixed initial parameters, which are not adaptive to different input images, thereby limiting their ability to produce the best-possible encrypted image. This paper introduces a new Harris Hawk optimization (HHO) based adaptive image encryption algorithm that integrates Hilbert vibrational decomposition (HVD), and multiple chaotic maps. The new fitness function is specifically designed to optimize the security and robustness of the encrypted image, considering statistical and differential attack parameters (NPCR, UACI, Entropy, and correlation coefficient values). HHO is used to optimize the initial parameters of chaotic maps (Henon map, Duffing map, Lorenz equation) subject to variations in the input image. In the confusion stage, the input image is decomposed into four components of same dimension but decreasing energy using HVD. Subsequently, the block based pixel scrambling is performed on alternate components using the Henon map and Duffing map. In the diffusion stage, the image from the confusion stage undergoes a first level diffusion by bitwise XOR operation with a random image generated from the Lorenz equation. Further, second level of diffusion is achieved with a complex operation generated image from the input image, enhancing its adaptability. Finally, Arnold Cat map having number of iterations determined by the input image is used for final pixel permutation to produces the encrypted image. The optimized initial parameters and input image dependent confusion and diffusion operations enable the encryption system to adapt to changes in the input image, thereby enhancing its resistance against known plain text and known cipher text attacks. Moreover, the integration of HVD with multiple chaotic maps makes the scheme more complex to decrypt with plain text attacks. The performance of proposed algorithm is evaluated on five standard test images in terms of security and quality measures. Experimental results exhibit statistical and differential attack parameters close to ideal values (with Max. Entropy≈7.9996, Min. CC≈ 10−5, Max. NPCR≈99.64, Max. UACI≈33.62). The use of multiple chaotic maps at different stages makes the key space larger. Robustness analysis demonstrates the algorithm's capability in withstanding noise and image enhancement attacks. The comparison with state of the arts metaheuristic based and classical encryption algorithms highlights the superior performance of proposed HHO driven algorithm. The significant improvement in security parameters and overall fitness compared to fixed initial parameters is achieved by optimizing the encryption process.

An Overview of Postprocessing in Quantum Key Distribution

Quantum key distribution (QKD) technology is a frontier in the field of secure communication, leveraging the principles of quantum mechanics to offer information-theoretically secure keys. Postprocessing is an important part of a whole QKD system because it directly impacts the secure key rate and the security of the system. In particular, with the fast increase in the photon transmission frequency in a QKD system, the processing speed of postprocessing becomes an essential issue. Our study embarks on a comprehensive review of the development of postprocessing of QKD, including five subprotocols, namely, parameter estimation, sifting, information reconciliation, privacy amplification, and channel authentication. Furthermore, we emphasize the issues raised in the implementation of these subprotocols under practical scenarios, such as limited computation or storage resources and fluctuations in channel environments. Based on the composable security theory, we demonstrate how enhancements in each subprotocol influence the secure key rate and security parameters, which can provide meaningful insights for future advancements in QKD.

Подход к оцениванию функциональности доверенных программно-аппаратных комплексов

The main provisions of a methodological approach for the functionality evaluating of trusted software and hardware systems according to its functional tasks performance indicators are presented in the article. The main purpose of the study is to create a critical objects functionality indicators monitoring system. The approach proposed is to assess the functionality status in conditions both precisely and vaguely defined parameters of information security, operability and reliability of trusted software and hardware systems.

Gendered perspective on water security, rights and conflicts in sub-Saharan Africa: a systematic review

The 2017 Global Risk Report ranked water crisis third among the top six global risks that greatly impact society. While the water crisis is disproportionately distributed worldwide, sub-Saharan Africa (SSA) has more people (most of whom are women) who face water problems compared to other regions worldwide. Through the application of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol version 2020, a systematic review examining the connection between gender and water security, rights, and water conflicts in SSA was done. A total of 24 out of 229 articles made it into the final analysis. Previous research studies on gender politics in the water sector have done so in a fragmented way, focusing on specific water security parameters. The emerging findings show that 14 years after the adoption of an enforceable human right to water and about 6 years prior to the cut-off date for the 2030 Agenda for Sustainable Development, matters of gender and water in SSA are far from being addressed. Rural people, especially women, still face multiple water-related challenges and conflicts, including discriminatory practices upholding male hegemony in water governance. It is recommended that actors in the water sector precede the implementation of water projects by understanding contextual settings that influence gendered water security. Furthermore, the implementation of the water Sustainable Development Goal (SGD 6) should be done in the context of SDG 5 (Gender Equity) and the prioritization of procedural water justice and governance, access to technical skills, and water conveyance technologies.Systematic review registrationhttps://www.prisma-statement.org/.

Secured lightweight authentication for 6LoWPANs in machine-to-machine communications

The development of machine-to-machine (M2M) technologies is becoming increasingly important in the rapidly growing domain of wireless sensor networks (WSNs) and the Internet of Things (IoT). Adopting IPv6 over 6LoWPANs (Low-Power Wireless Personal Area Networks) is instrumental in communicating across diverse domains within WSNs, albeit with its challenges. Particularly, resource limitations and security vulnerabilities remain significant concerns. 6LoWPAN-based M2M protocols that rely on authentication and key establishment schemes (AKE) often fall short due to inadequate security issues and excessive resource requirements. This paper addresses these challenges by introducing a secure and resource-efficient framework—Lightweight AKE for 6LoWPAN Nodes (LAKE-6LN). LAKE-6LN capitalizes on the clustering architecture's merits and contrasts conventional router-centric approaches. To ensure lightweight and efficient operation, it uses hash functions, XOR functions, and symmetric encryption techniques. Pseudo-identity, sequence tracking numbers, and secure parameters ensure privacy and protection against attacks, including traceability, perfect forward secrecy, ephemeral secret leakage, and secure the session key. An informal analysis of LAKE-6LN's security confirms that compliance with all essential security properties has been achieved. In addition, the framework's logical robustness and security analysis are rigorously verified using BAN logic, AVISPA, and Scyther tools. LAKE-6LN has demonstrated superior performance over related schemes, demonstrating a reduction in storage costs (by 33.33 % to 85.71 %), computational overhead (by 14.28 % to 95.97 %), communication overhead (by 16.12 % to 51.85 %), and energy consumption (by 22.04 % to 99.40 %). In our comparative analysis, LAKE-6LN demonstrates its resilience against various security threats, demonstrating its potential to secure 6LoWPAN networks in M2M.

Secure-by-Design Real-Time Internet of Medical Things Architecture: e-Health Population Monitoring (RTPM)

The healthcare sector has undergone a profound transformation, owing to the influential role played by Internet of Medical Things (IoMT) technology. However, there are substantial concerns over these devices’ security and privacy-preserving mechanisms. The current literature on IoMT tends to focus on specific security features, rather than wholistic security concerning Confidentiality, Integrity, and Availability (CIA Triad), and the solutions are generally simulated and not tested in a real-world network. The proposed innovative solution is known as Secure-by-Design Real-Time IoMT Architecture for e-Health Population Monitoring (RTPM) and it can manage keys at both ends (IoMT device and IoMT server) to maintain high privacy standards and trust during the monitoring process and enable the IoMT devices to run safely and independently even if the server is compromised. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices’ overheads, and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system, i.e., the CIA Triad, and conducts device authentication, protects from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, and prevents non-repudiation attacks in real time. A self-healing solution during the network failure of live e-health monitoring is also incorporated in RTPM. The robustness and stress of the system are tested with different data types and by capturing live network traffic. The system’s performance is analysed using different security algorithms with different key sizes of RSA (1024 to 8192 bits), AES (128 to 256 bits), and SHA (256 bits) to support a resource-constraint-powered system when integrating with resource-demanding secure parameters and features. In the future, other security features like intrusion detection and prevention and the user’s experience and trust level of such a system will be tested.

Mechanism of increasing the efficiency of functioning of raw material zones of agroindustrial production

The paper presents the results of the analysis of the main organisational and economic models of functioning of raw material zones of agroindustrial production, identifies key features, advantages in the current conditions of the Republic of Belarus and the tasks of improving the current mechanism. The criteria of formation (maximum provision of demand for agricultural raw materials with regard to the optimal utilisation of production capacities and target parameters of food security; increasing the comparative efficiency of agricultural raw materials production in terms of yield (productivity), unit production cost and quality; building up added value within the product chain, etc.) and principles of functioning of highly efficient raw material zones (strategic partnership; increasing competitiveness of agricultural raw material zones) are substantiated.

Synchronous Distributed Key Generation without Broadcasts

Distributed key generation (DKG) is a key building block in developing many efficient threshold cryptosystems. This work initiates the study of communication complexity and round complexity of DKG protocols over a point-to-point (bounded) synchronous network. Our key result is the first synchronous DKG protocol for discrete log-based cryptosystems with O ( κ n 3 ) communication complexity ( κ denotes a security parameter) that tolerates any t < n / 2 Byzantine faults among n parties. We present two variants of the protocol: (i) a protocol with worst-case O ( κ n 3 ) communication and O ( t ) rounds, and (ii) a protocol with expected O ( κ n 3 ) communication and expected constant rounds. In the process of achieving our results, we design (1) a novel weak gradecast protocol with a communication complexity of O ( κ n 2 ) for linear-sized inputs and constant rounds, (2) a protocol called “recoverable-set-of-shares” for ensuring recovery of shared secrets, (3) an oblivious leader election protocol with O ( κ n 3 ) communication and constant rounds, and (4) a multi-valued validated Byzantine agreement (MVBA) protocol with O ( κ n 3 ) communication complexity for linear-sized inputs and expected constant rounds. Each of these primitives is of independent interest.

FedSHE: privacy preserving and efficient federated learning with adaptive segmented CKKS homomorphic encryption

Unprotected gradient exchange in federated learning (FL) systems may lead to gradient leakage-related attacks. CKKS is a promising approximate homomorphic encryption scheme to protect gradients, owing to its unique capability of performing operations directly on ciphertexts. However, configuring CKKS security parameters involves a trade-off between correctness, efficiency, and security. An evaluation gap exists regarding how these parameters impact computational performance. Additionally, the maximum vector length that CKKS can once encrypt, recommended by Homomorphic Encryption Standardization, is 16384, hampers its widespread adoption in FL when encrypting layers with numerous neurons. To protect gradients’ privacy in FL systems while maintaining practical performance, we comprehensively analyze the influence of security parameters such as polynomial modulus degree and coefficient modulus on homomorphic operations. Derived from our evaluation findings, we provide a method for selecting the optimal multiplication depth while meeting operational requirements. Then, we introduce an adaptive segmented encryption method tailored for CKKS, circumventing its encryption length constraint and enhancing its processing ability to encrypt neural network models. Finally, we present FedSHE, a privacy-preserving and efficient Federated learning scheme with adaptive Segmented CKKS Homomorphic Encryption. FedSHE is implemented on top of the federated averaging (FedAvg) algorithm and is available at https://github.com/yooopan/FedSHE. Our evaluation results affirm the correctness and effectiveness of our proposed method, demonstrating that FedSHE outperforms existing homomorphic encryption-based federated learning research efforts in terms of model accuracy, computational efficiency, communication cost, and security level.

Secure Travel Planning Using a Heuristic Algorithm

Security perception in the urban area has a significant effect on travel behaviour, preferences, and tour planning. The perceptions of security risks can vary depending on factors, such as age, gender, and previous experiences. This study aims to consider security risks when developing travel plans and schedules for various activities. An improved heuristic algorithm, based on the Travelling Salesman Problem with security parameter and flexibility aspects, is proposed. Public transport is considered in three situations: fixed, flexible, and flexible-security situations. The outcomes demonstrate that the flexible situation significantly decreases travel times by 21% compared to the basic (fixed) situation. At the same time, with a slightly increased travel time the security risk can be avoided in the flexible-security situation. Travelers can enjoy a higher quality of travelling and enhanced personal experience by minimizing the journey duration and the impact of security risks on the tour schedule. The proposed method provides significant benefits for transport operators by increasing the efficiency of the transportation system and higher customer satisfaction.

More Efficient Two-Round Multi-Signature Scheme with Provably Secure Parameters for Standardized Elliptic Curves

More Efficient Two-Round Multi-Signature Scheme with Provably Secure Parameters for Standardized Elliptic Curves