High Level Of Security Research Articles

Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network's security and integrity. This paper proposes a novel method-a Composite Ensemble Learning (CEL)-based neural network-for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.

Secure image communication based on two-layer dynamic feedback encryption and DWT information hiding.

In response to the vulnerability of image encryption techniques to chosen plaintext attacks, this paper proposes a secure image communication scheme based on two-layer dynamic feedback encryption and discrete wavelet transform (DWT) information hiding. The proposed scheme employs a plaintext correlation and intermediate ciphertext feedback mechanism, and combines chaotic systems, bit-level permutation, bilateral diffusion, and dynamic confusion to ensure the security and confidentiality of transmitted images. Firstly, a dynamically chaotic encryption sequence associated with a secure plaintext hash value is generated and utilized for the first round of bit-level permutation, bilateral diffusion, and dynamic confusion, resulting in an intermediate ciphertext image. Similarly, the characteristic values of the intermediate ciphertext image are used to generate dynamically chaotic encryption sequences associated with them. These sequences are then employed for the second round of bit-level permutation, bilateral diffusion, and dynamic confusion to gain the final ciphertext image. The ciphertext image hidden by DWT also provides efficient encryption, higher level of security and robustness to attacks. This technology offers indiscernible secret data insertion, rendering it challenging for assailants to spot or extract concealed information. By combining the proposed dynamic closed-loop feedback secure image encryption scheme based on the 2D-SLMM chaotic system with DWT-based hiding, a comprehensive and robust image encryption approach can be achieved. According to the results of theoretical research and experimental simulation, our encryption scheme has dynamic encryption effect and reliable security performance. The scheme is highly sensitive to key and plaintext, and can effectively resist various common encryption attacks and maintain good robustness. Therefore, our proposed encryption algorithm is an ideal digital image privacy protection technology, which has a wide range of practical application prospects.

Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks

Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%.

Photochromic Spiropyran-Based Dual-Emitting Luminescent Hybrid Films for Dynamic Information Anticounterfeiting.

Photoluminescent materials are widely used for information storage and anticounterfeiting, while most of them have the disadvantages of static information performance and weak processability, which is still a challenging task in developing dynamic anticounterfeiting materials with high security levels. Herein, we fabricated a novel photostimuli-responsive dual-emitting luminescent material UPTES-SPn-Tb-hfa, which was obtained by introducing the photochromic molecule spiropyran (SP) and lanthanide complex (Tb-hfa) into a siloxane-polyether matrix using the sol-gel process. Due to the conformation-dependent photochromic fluorescence resonance energy transfer between the Tb-hfa donor and SP acceptor, the ring-closing (SP)/ring-opening (MC) isomerization of the SP unit leads to a reversible luminescence switching in UPTES-SPn-Tb-hfa. This composite material has great potential for advanced anticounterfeiting because of the advantage of rapidly repeatable encryption/decryption for at least 8 times and dynamic luminescent colors within 15 s. In addition, due to its two luminescent centers (Tb3+ and MC), the luminescent color of this material can be regulated by 254 and 365 nm UV-light irradiation, which facilitates the design of multicolored anticounterfeiting labels. Our work presents a novel design methodology to fabricate dynamic anticounterfeiting materials, significantly enhancing the security of anticounterfeiting applications.

Re-Lock: Penerapan Integrasi Smart Lock Menggunakan Image Processing Berbasis Artificial Intelligence Menuju Indonesia Emas 2045

Golden Indonesia 2045 is a long-term vision that targets Indonesia to become a developed country with high levels of welfare and security. One important aspect in achieving this vision is the application of advanced technology to support security and efficiency in various sectors. This article aims to discuss the application of smart lock integration using artificial intelligence (AI)-based image processing technology, called “Re-Lock”. Re-Lock technology is designed to enhance physical security through advanced visual identification, allowing access only to authorized individuals based on real-time image analysis. This research adopts a system development method that integrates AI with smart locks, where image processing is used to recognize faces, fingerprints, or certain patterns as a form of identity verification. Case studies were applied to smart home environments and public facilities to test the reliability and efficiency of the system under real conditions. The results show that Re-Lock has a high accuracy rate in identifying individuals and is able to significantly reduce the risk of unauthorized access. In addition, the system is also designed to be easily integrated with existing infrastructure, making it a practical and affordable solution in supporting national security in the digital era. The implementation of Re-Lock is projected to contribute significantly to the achievement of the Golden Indonesia 2045 vision, by making security technology one of the main pillars in the country's development.

Polymer Free Volume-Controlled Molecular Clock and Emitter for Multicolored Transient Data Display in Advanced Photonic Cryptography.

The booming demand on data security has aroused great interest for developing smart materials with temporal display feature and dynamic multicolor fluorescence. However, it remains challenging to implement both features on most responsive molecules. Herein, we construct a polymer free volume-controlled "molecular clock and emitter" via covalently embedding a multi-stimuli responsive molecular switch (i.e., spiropyran) into a polymer network (i.e., poly(pentafluorophenyl acrylate)) with programmable crosslink density and free volume. By the aminolysis of pentafluorophenyl ester with different amount of diamine crosslinkers, pPFPA-co-SP networks with controllable crosslink densities are generated, which have different confinement effects on the rate constant of SP/MC isomerization, thus leading to time-dependent photochromism. In addition, PTF1, a fluorescent probe that is sensitive to polymer rigidity, is introduced to further endow pPFPA-co-SP system with phototunable dynamic full-color emission. Therefore, relying on their synergistical responses to the rigidity of the polymer network, we have successfully developed a versatile molecular clock and emitter via an "one stone two birds" manner, which shows time-dependent data display along with dynamic multicolor fluorescence switching, providing great potential for advanced encryption and anticounterfeiting with a high security level.

Polymer Free Volume‐Controlled Molecular Clock and Emitter for Multicolored Transient Data Display in Advanced Photonic Cryptography

AbstractThe booming demand on data security has aroused great interest for developing smart materials with temporal display feature and dynamic multicolor fluorescence. However, it remains challenging to implement both features on most responsive molecules. Herein, we construct a polymer free volume‐controlled “molecular clock and emitter” via covalently embedding a multi‐stimuli responsive molecular switch (i.e., spiropyran) into a polymer network (i.e., poly(pentafluorophenyl acrylate)) with programmable crosslink density and free volume. By the aminolysis of pentafluorophenyl ester with different amount of diamine crosslinkers, pPFPA‐co‐SP networks with controllable crosslink densities are generated, which have different confinement effects on the rate constant of SP/MC isomerization, thus leading to time‐dependent photochromism. In addition, PTF1, a fluorescent probe that is sensitive to polymer rigidity, is introduced to further endow pPFPA‐co‐SP system with phototunable dynamic full‐color emission. Therefore, relying on their synergistical responses to the rigidity of the polymer network, we have successfully developed a versatile molecular clock and emitter via an “one stone two birds” manner, which shows time‐dependent data display along with dynamic multicolor fluorescence switching, providing great potential for advanced encryption and anticounterfeiting with a high security level.

High-security holographic display with content and copyright protection based on complex amplitude modulation

The computer-generated hologram provides an approach to modulate the coherent wavefront and has been widely applied in holographic displays. In the actual application, holograms need to be transmitted through the network, which results in the illegal acquisition and malicious manipulation of holographic images. Therefore, it is necessary to develop a practical method to protect the content and copyright of holographic images. In this paper, we develop a high-security holographic display method based on complex amplitude modulation. In our proposed method, the complex hologram of a holographic image is decomposed into two phase-only holograms, one of which is designed as the ciphertext, and the other is regarded as the key. As a result, the holographic image can be reconstructed only when the ciphertext and the key are paired, which boosts the security of the holographic image. Meanwhile, the copyright of the holographic image is protected via a watermark that is embedded in the ciphertext in the form of a hologram. Due to the simultaneous use of encryption and watermark technology, our proposed method could transmit holographic images at a high security level, and has great potential to be applied in holographic displays in the future.

Perceptions Toward Telemedicine of Health Care Staff in Nursing Homes in Northern Germany: Cross-Sectional Study.

Digitalization in the German health care system is progressing slowly, even though it offers opportunities for improvement of care. In nursing homes, most of the staff's work is paper based. Following the pandemic, there has been a decrease in the use of telemedicine applications. To ensure long-term implementation, the views of users, in this case nurses, are of interest. This cross-sectional study was conducted to describe which digital applications are already being used at inpatient care facilities, the attitude of nurses toward telemedicine, and for which areas the use of telemedicine in the facilities is considered appropriate by the participants. All inpatient care facility staff in Schleswig-Holstein were invited to participate in the survey from August 1 to October 31, 2022. The questionnaire consists of 17 determinants that ask about the attitude, use, and possible applications of telemedicine. In addition to a descriptive analysis, the influence of the general attitude toward telemedicine on various determinants was examined using the Fisher exact test for nominal variables and Spearman correlation coefficient for metric variables. A total of 425 caregivers participated in the survey. Of these respondents, 10.7% (n=41) currently used video consultations, and 76.1% (n=321) of the respondents were in favor of video consultations being practiced in training. Furthermore, 74.8% (n=312) of the respondents would attend a training on telephone medical consultation. Respondents indicated that video consultations have a small added value compared to asynchronous telemedicine (eg, sending photos). However, video consultations were perceived as somewhat less time-consuming than other communication channels. Video consultations are perceived as most useful for clarifying urgent problems. The respondents estimated that one in five paramedic calls at their facilities could be reduced through telemedicine approaches. It was important to the participants that telemedicine is as simple as possible and that there is a high level of data security. Although many caregivers have a positive attitude toward telemedicine and perceive its advantages, communication channels such as video consultation are still used infrequently in care facilities. To promote the use of telemedicine applications, it is important to emphasize their benefits. The presumed saving of paramedic calls thus represents a benefit, and it is crucial to train caregivers in the use of telemedicine to avoid uncertainties in dealing with the newer technologies. It is important to give them enough time and repetitions of the training.

IDMap: Leveraging AI and Data Technologies for Early Cancer Detection

Cancer screening is vital in cutting mortality rates, and containing the impact of cancer in a worldwide basis. The current conventional detection techniques including imaging and biopsy though efficient are also characterized with drawbacks like; invasive, expensive, and inaccurate. This abstract will describe the new AI and data solution in the fight against early cancer detection, which presents a massive opportunity to improve accuracy, cut down the time that it takes to deliver a diagnosis, and bring quality health care to possibly millions of patients. ML and, in particular, DL are prospective in terms of decision making upon medical data including imaging, genomic sequences, and electronic health records to detect biomarkers of cancer in early stages. The statistics show that the AI-driven systems are capable to provide better diagnostic outcomes than conventional methods in some fields including mammography for breast cancer and CT for lung. Moreover, AI’s integration in genomic studies helps in determining Cancer related genes and biomarkers hence supporting precision medicine that adapts treatment to the specific genetic information of the patient. Apart from having outlets in AI, big data analytics, cloud computing, and IoT are equally important in early cancer detection as well. Big data analysis enables the analysis of large and complicated data sets with the aid of which one may identify inklings that may point towards possible early development of cancer. The use of cloud computing in health care mainly provides meaningful platforms for the storage and management of the large volumes of medical data in a way that allows improved efficiency and high levels of security. Wearable sensors collect data on different biomarkers throughout a patient’s body, and convey real-time information regarding whether the biomarkers’ levels are approaching cancerous state. Despite this great promise, there are various issues that have to be solved: data protection, privacy, and security, problems with the algorithms’ biases, and integration into practice. Ethical questions are generally important to tackle the uncertainty surrounding data and decision-making in clinical care using A I systems. The future trends in early cancer diagnostics will involve deeper integration of the approaches as AI and big data technology, which will enable more precise prevention and treatment. The applicability of this approach can also extend to the early identification of cancer, but also the prevention of its occurrence through proper intervention. In conclusion, conversing AI and data technologies will be useful for enhancing the efficiency of the early cancer diagnosis, and that is why the perspectives for the patients’ recovery and the further decrease in the mortality rates connected with cancer are rather promising. The approaches in this area remain informative developing technologies that are likely to be integrated into clinical work as the leading organizational models for the future of oncology and preventive health.

Integrating Blockchain technology within an Information Ecosystem

ContextBlockchain-based Information Ecosystems (BBIEs) are a type of information ecosystem in which blockchain technology is used to provide a trust mechanism among parties and to manage shared business logic, breaking the traditional scheme of Information Ecosystems dominated by a leading company and leveraging the decentralization of data management, information flow, and business logic. ObjectiveIn this paper, we propose architecture and technical aspects concerning creating a BBIE, underlining the advantages supplied and the logic decomposition among the business and storage components. MethodThe requirements are derived from the current needs of the collaborative business and the data collected by surveying practitioners. To get these needs we followed the Grounded Theory research approach. We validate our architectural schema against a case study on managing a wine supply chain–involving different companies and supervision authorities. ResultsThe proposed solution integrates blockchain-based applications with the existing information system as a module of the ecosystem, leveraging on the low costs, scalability, and high-level security because of the restricted access to the network. ConclusionWe must go a long way in deepening and refining the possibilities offered by technology in supporting innovative multi-organizational business models. BBIEs can contribute substantially to paving the way in such a direction.

A secure fractal compression scheme based on irregular Latin square, Julia and 2D-FCICM

With the proliferation of digital image data, ensuring its secure and efficient storage and transmission has become important. One-dimensional (1D) chaotic maps come with their set of limitations, such as chaotic interval discontinuity and simple chaotic behavior. To increase the complexity of chaotic systems, this paper proposed a novel 2D fractal coupled model (2D-FCM) to generate chaotic systems and extend it to n-dimensional chaotic systems. Meanwhile, the newly generated 2D-FCICM is employed in secure fractal compression. Compared to existing chaotic systems, the pseudo-random sequences generated by the 2D-FCICM exhibit better randomness and complexity. To enhance the plaintext sensitivity of the algorithm, a localized magnified fractal image is produced using plaintext information. Control parameters for the chaotic system are then derived from both the fractal and the original images. Given the compressed data's I × 5 size, this study proposed a technique for generating irregular Latin squares and integrated it into the scrambling phase. The experimental findings indicate that this scheme boasts high security levels, strong plaintext sensitivity and key sensitivity, and is resilient against various statistical attacks.

Interfacial Wrinkling Structures Based on a Double Cross-Linking Strategy Enable a Dual-Mode Optical Information Encryption.

Surface wrinkling structures based on a bilayer system are widely employed in storing and encrypting specific optical information. However, constructing a stable wrinkling structure with high-level security remains an extensive challenge due to the delamination issue between the skin layer and the substrate. Herein, a double cross-linking strategy is introduced between a hydrogel layer doped with fluorescent molecules and polydimethylsiloxane to establish a stable interfacial wrinkling structure with dual-mode functionality, in which the light reflection of the wrinkles and fluorescence intensity of fluorescent molecules can be simultaneously regulated by the modulus ratio between the two layers. The spontaneous wrinkling structures with a physically unclonable function can enhance the photoluminescence emission intensity of the wrinkling area under ultraviolet radiation. Meanwhile, the skin layer constructed of acrylamide and acrylic acid copolymer protects the interfacial wrinkling patterns from the loss of a detailed structure for authentication due to external damage. The stable interfacial wrinkling structures with fluorescence can find potential applications in the fields of information storage and encryption.

Conceptional Pure-Tungsten Metasurfaces Based on Femtosecond Laser Nanomanufacturing

A laser-induced periodic surface structure (LIPSS), which can be easily produced by femtosecond laser ablation, is a unique nanostructure with a visible refractive color that can be controlled by altering its orientation and uniformity, making it suitable for use in colorful marking, camouflage, and anti-counterfeiting measures. However, single-mode information camouflage can no longer meet increasingly higher-level security requirements. Therefore, metasurfaces offer revolutionary solutions. In this study, conceptual metasurfaces of pure tungsten are theoretically proposed and verified using hierarchical LIPSS/nanoparticle (NP) nanostructures as meta-atoms. The anisotropy of the LIPSS nanostructure enables polarization-sensitive optical modulation, whereas the spatial configuration, particle size, and period of LIPSS in the LIPSS/NP meta-atoms provide flexibility for tailoring broadband optical responses. In x-polarization, the LIPSS/NP meta-atom system provides more visible colors and divergent infrared absorbance (emission) than in y-polarized and unpolarized modes, paving the way for vividly colorful polarization-sensitive displays and information camouflage in infrared bands. A simplified rendition of the world-famous painting “The Starry Night” by Van Gogh is used as a proof-of-concept. Preliminary experimental results are presented, based on which the feasibility and challenges for laser nanomanufacturing of the proposed conceptual metasurfaces are discussed, aiming to provide inspiration for the development of novel metasurfaces through interdisciplinary studies.

ANALYSIS OF BLOCKCHAIN TYPES AND THEIR SUITABILITY FOR IMAGE STORAGE

There different types of blockchains and their possible use for creating an image repository are investigated. The purpose of the study was to evaluate the advantages and limitations of different types of blockchains in terms of image storage. Data processing methods were applied to analyze the technical characteristics of various types of blockchains and comparative analysis of efficiency and reliability parameters. The results were obtained, which made it possible to formulate the principles of choosing the type of blockchain for creating image storage and to identify the advantages and limitations of each type from the point of view of image storage, depending on the priorities of the software product. The conclusion is that the use of blockchain provides a high level of security and integrity of images, some types of blockchains exhibit high speed and scalability. However, it is important to understand that the preservation process may remain centralized, so more research is needed to optimally use and develop these technologies. Future research may include an analysis of the possibilities of ensuring the privacy of participants and the development of standards for the sharing of multimedia content via blockchain. It is important to consider that the use of blockchain can contribute to increasing transparency and trust in the process of storing and sharing multimedia content, which is important for the development of the digital economy. However, in order to achieve the full potential of blockchain in the field of multimedia, it is necessary to develop effective strategies to solve the problems of privacy, scalability and centralization that arise when implementing these technologies. Such a comprehensive approach will provide a stable and effective infrastructure for managing multimedia content in the digital environment.

ЕФЕКТИВНІ СХОВИЩА ДАНИХ ДЛЯ РІШЕНЬ МАШИННОГО НАВЧАННЯ

Awareness of the importance of using effective data warehouses to develop machine learning solutions in today's world is becoming increasingly relevant in connection with the growth of data volumes and the growing need for accurate and productive models. The article identifies major vital requirements for data storage systems to ensure effective data management in building machine learning solutions. The study underscores the practical implications of rapid data access and minimal latency, reliable storage systems with robust backup and recovery mechanisms, high-level security, flexibility in handling different data types, and cost-effectiveness. These factors are not just theoretical considerations but directly impact the efficiency and success of machine learning solutions. Together with describing the concepts of block storage and object storage, the article also compares major cloud platforms—AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP)—each providing a comprehensive range of flexible and customizable storage, security, and data processing services. These platforms enable users to manage their information resources flexibly and efficiently, offering unique features that cater to the specific needs of machine learning applications. Understanding the nuances of each provider's offerings empowers engineers and researchers to make informed decisions that align with their unique requirements. The article provides a thorough overview of the key considerations and available options, guiding strategic decisions to support machine learning initiatives effectively. Also the importance of staying up to date with the evolution of major platforms offerings is emphasized. In conclusion, selecting the proper data storage solution is critical for enhancing the performance, security, and cost-efficiency of machine learning models.

Job insecurity and fertility: Evidence from massive lay-offs in urban China

We exploit a staggered reform of state-owned enterprises (SOEs) in the late 1990s in China to provide plausibly causal evidence that job insecurity has a first-order impact on fertility. Prior to the reform, unemployment rates were low and job security for SOE workers paralleled that of government employees. Post-reform, numerous SOE employees were laid off and their contracts were no longer permanent, but government employees continued to enjoy high levels of job security. We find that the reform caused SOE employees who retained their positions to delay having their first child by 0.718 years. The spillover effects are sizable: employees in the untargeted private sector delay starting a family by 0.387 years. Despite the importance of family lineage in China at the time, our findings indicate that the fertility response transcended mere birth timing adjustments, and decreased couples’ likelihood of having children. Specifically, the reform initially reduced the number of births by 8.4 % in the short run and had a more pronounced long-term effect on completed fertility at age 45.

Legal Aspects of Using Blockchain Technologies in the Field of E-Commerce on SDG'S

Objective: The objective of this study is legal aspects of blockchain. Theoretical Framework: Blockchain is one of the most prominent technologies and it has become a driving factor in business transformation. Method: Methods of historical and logical analysis and comparison, expert opinions, system analysis and forecasting were used. The research materials included legal aspects, in particular, in the development of blockchain technology. Results and Discussion: Blockchain, like any electronic technology, can have different forms of social implementation. Their openness is manifested in the fact that anyone can become a network node participating in the consensus procedure. Information in public blockchains is actually and legally publicly available. The listed qualities ensure a high level of security of public blockchains and a high level of users’ trust toward them. Research Implications: The legal regulation of digital technologies is becoming an important tool for ensuring a balance between public and private interests, benefits and risks of the digital age. Originality/Value: The global blockchain market will grow significantly in the coming years. The new vector of blockchain technology will be aimed at accelerating the pace of digitalization, financial inclusion and enhancing security. There is no doubt that blockchain is a disruptive technology and will continue to dominate the business world in the coming years.

Hyperentanglement quantum communication over a 50 km noisy fiber channel

High-dimensional entanglement not only offers a high security level for quantum communication but also promises improved information capacity and noise resistance of the system. However, due to various constraints on different high-dimensional degrees of freedom, whether these advantages can bring improvement to the actual implementation is still not well proven. Here we present a scheme to fully utilize these advantages over long-distance noisy fiber channels. We exploit polarization and time-bin hyperentanglement to achieve high-dimensional coding, and observe significant enhancements in secure key rates and noise tolerance that surpass the capabilities of qubit systems. Moreover, the demonstration achieves a distribution up to 50 km, which is the longest distance for high-dimensional entanglement-based quantum key distribution up to date, to our knowledge. Our demonstration validates the potential of high-dimensional entanglement for quantum communications over long-distance noisy channels, paving the way for a resilient and resource-efficient quantum network.

Fast and Efficient UserID Lookup in Distributed Authentication: A Probabilistic Approach Using Bloom Filters

Purpose: User authentication in distributed systems presents unique challenges due to the decentralized nature of these environments and the potential for high-volume login attempts. This paper proposes an efficient method for UserID existence checking during the login process using Bloom filters, a space-efficient probabilistic data structure. Our approach aims to reduce authentication latency and minimize network traffic while maintaining a high level of security. Methodology: We present a novel system architecture that incorporates Bloom filters at strategic points within the distributed system to perform rapid preliminary checks on UserID existence. This method allows for quick rejection of non-existent UserIDs without querying the main user database, significantly reducing the load on central authentication servers. The paper details the implementation of Bloom filters optimized for UserID storage and lookup, including considerations for filter size, hash function selection, and false positive rate management. We also describe the integration of this method into a typical authentication workflow, highlighting the points at which Bloom filter checks are performed and how they interact with existing security measures. Findings: To evaluate the effectiveness of our approach, we conducted extensive experiments simulating various scales of distributed systems and login attempt patterns. Our results demonstrate that the Bloom filter-based UserID existence checking method reduces authentication latency by an average of 37% compared to traditional database lookup methods. Additionally, we observed a 42% decrease in network traffic related to authentication processes, indicating improved scalability for large-scale distributed systems. The paper also discusses the trade-offs inherent in using probabilistic data structures for security-critical operations, addressing potential vulnerabilities and proposing mitigation strategies. We conclude by outlining future research directions, including adaptive Bloom filter sizing and the potential application of this method to other aspects of distributed system security. Unique Contribution to Theory, Policy and Practice: This research contributes to the field of distributed systems security by providing a practical, efficient, and scalable solution for UserID existence checking, potentially improving the performance and user experience of large-scale authentication systems.