Academic literature on the topic 'Dynamic Trust Scoring'
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Journal articles on the topic "Dynamic Trust Scoring"
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Park, Ui Hyun, Jeong-hyeop Hong, Auk Kim, and Kyung Ho Son. "Endpoint Device Risk-Scoring Algorithm Proposal for Zero Trust." Electronics 12, no. 8 (2023): 1906. http://dx.doi.org/10.3390/electronics12081906.
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The rapid expansion of remote work following the COVID-19 pandemic has necessitated the development of more robust and secure endpoint device security solutions. Companies have begun to adopt the zero trust security concept as an alternative to traditional network boundary security measures, which requires that every device and user be considered untrustworthy until proven otherwise. Despite the potential benefits of implementing zero trust, the stringent security measures can inadvertently lead to low availability by denying access to legitimate users or limiting their ability to access necessary resources. To address this challenge, we propose a risk-scoring algorithm that balances confidentiality and availability by evaluating the user’s impact on resources. Our contributions include (1) summarizing the limitations of existing risk scoring systems in companies that implement zero trust, (2) proposing a dynamic importance metric that measures the importance of resources accessible to users within zero trust systems, and (3) introducing a risk-scoring algorithm that employs the dynamic importance metric to enhance both security and availability in zero trust environments. By incorporating the dynamic importance metric, our proposed algorithm provides a more accurate representation of risk, leading to better security decisions and improved resource availability for legitimate users. This proposal aims to help organizations achieve a more balanced approach to endpoint device security, addressing the unique challenges posed by the increasing prevalence of remote work.
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Parmar, Digvijay. "Dynamic Trust Score Explanation and Adjustment in Zero Trust Architecture Using Large Language Models." Journal of Artificial Intelligence Research 5, no. 1 (2025): 1–30. https://doi.org/10.5281/zenodo.15524788.
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Dynamic trust scoring in ZTA enables frequent risk assessment using constant inputs from various security sources. Multiple data sources are combined to compute a continuously updated score reflecting the level of trust in a given access or transaction. The study utilizes explainable Large Language Models (LLMs) to generate comprehensible explanations for why trust levels are altered. The model leverages a RAM pipeline to consolidate diverse security signals, enhance them with contextual data, and generate human-readable justifications explaining trust score updates. The system associates the explanations with corresponding ZTA policies, allowing it to perform security measures like two-factor authentication prompts, elimination of access requests, and segregation of devices. Practical applications have demonstrated that the approach successfully handles suspicious login attempts and identifies misuse of critical assets. Adding LLM-generated explanations to ZTA has shown to improve the timeliness and accuracy of security decisions and makes the system better prepared for emerging cyber risks and threats.
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Chaoqun Kang, Chaoqun Kang, Erxia Li Chaoqun Kang, Dongxiao Liu Erxia Li, Xinhong You Dongxiao Liu, and Xiaoyong Li Xinhong You. "A Dynamic and Fine-Grained User Trust Evaluation Model for Micro-Segmentation Cloud Computing Environment." 電腦學刊 34, no. 4 (2023): 215–32. http://dx.doi.org/10.53106/199115992023083404019.
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<p>With the diversity and complexity of user access behaviors in the &ldquo;micro-segmentation&rdquo; cloud computing environment, it is no longer possible to control unauthorized access of authorized users by only relying on user identity login authentication to control user access to cloud resources. The existing trust evaluation methods can not cope with the characteristics of &ldquo;micro-isolated&rdquo; cloud environment, which is characterized by high granularity of resources, increasing number of users&rsquo; access requests and rapid changes. Based on the zero-trust principle of &ldquo;Never trust, al-ways verify&rdquo;, we propose a dynamic, fine-grained user trust evaluation model for micro-segmentation cloud computing environment, which combines multiple user trust attributes and leverages the subjective-objective approach to assign weights to trust attribute indicators to achieve dynamic scoring of users&rsquo; real-time behaviors. To capture the characteristics of users&rsquo; intrinsic behaviors, we use correlation analysis to identify the correlation between users&rsquo; current and historical behaviors, and combine sliding windows and penalty functions to optimize the model. The massive simulation experiments demonstrate the effectiveness of the proposed dynamic and fine-grained method, which can effectively combine the intrinsic correlation of users&rsquo; own access behavior and the difference of access behavior among different users.</p> <p>&nbsp;</p>
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Vinod, Veeramachaneni. "Integrating Zero Trust Principles into IAM for Enhanced Cloud Security." Recent Trends in Cloud Computing and Web Engineering 7, no. 1 (2024): 78–92. https://doi.org/10.5281/zenodo.14162091.
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<em>This paper investigates the integration of Zero Trust principles into Identity and Access Management (IAM) frameworks to strengthen security in multi-cloud and hybrid cloud environments. Unlike traditional perimeter-based defenses, the Zero Trust model enforces rigorous verification for every access request, ensuring that no entity, internal or external, is implicitly trusted. Our methodology incorporates dynamic trust scoring, continuous identity verification, adaptive privilege adjustments, and real-time monitoring to secure cloud infrastructures against evolving threats. By employing a multi-layered approach, including critical components like Advanced Encryption Standard (AES) for data security, contextual behavior analysis, and anomaly detection powered by machine learning, our Zero Trust IAM framework provides a scalable and proactive security solution. Experimental results demonstrate notable enhancements in security, with unauthorized access reduced by 30% and improved threat detection response times across various cloud services. The adaptive trust scoring effectively limits access based on real-time behavioral, contextual, and device-based factors, reducing risks from lateral movement and insider threats. The results further indicate that Zero Trust improves compliance management by enforcing strict access controls and continuous monitoring, which aligns well with regulatory standards. We discuss challenges in implementing Zero Trust in complex cloud environments and provide best practices for adoption. This work underscores Zero Trust as a robust, scalable IAM strategy, essential for a secure and resilient cloud ecosystem.</em>
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Synko, Anna. "The method of trust level of publications hosted in virtual communities." Scientific journal of the Ternopil national technical university 105, no. 1 (2022): 68–79. http://dx.doi.org/10.33108/visnyk_tntu2022.01.068.
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The proposed model of data collection and analysis from thematic virtual communities using known information analysis techniques: scoring and parsing. Open communities were selected for the study, namely their architecture and main components: information content (title, description, posts, topics of the event) and audience (community members). To select relevant, informative, reliable publications, the scoring method is used which reflects the level of trust of the authors of the publication in the form of weighted indicators of a set of certain characteristics. Data collection is a combined approach, as virtual communities are dynamic in the content of the data and their content depends on the actions of the participants. To parse posts from virtual communities, it was decided to use ImportXML function in Microsoft Excel, which allows you to collect data from different sources, and then sample, analyze, and select the presentation of results using other built-in tools of this program.
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Rani, Jyoti, Rushan Gupta, Sameer Kumar Singh, Manik Sood, and Kuldeep Kumar Chauhan. "Federated Learning-Based Authentication and Trust Scoring System for Cloud IoT Security." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 412–18. http://dx.doi.org/10.22214/ijraset.2024.65079.
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Abstract: A number of IoT-related security challenges are faced due to the introduction of IoT devices in almost all sections, including smart homes and industrial applications. New security solutions will be required in IoT networks to tackle issues pertaining to privacy, scalability, and efficiency with solid robustness against potential vulnerabilities. Even though some occasions these are effective centralized security frameworks suffer from significant limitations, including single points of failure, increased latency, and risks associated with data centralization [1][2]. These problems worsen as IoT networks grow in size and complexity. The below is the Federated Learning-Based Authentication and Trust Scoring System proposal toward overcoming these challenges while keeping in view the benefits offered by FL. It enables IoT devices to jointly train a global model for anomaly detection without raw data sharing [3]. This happens in a distributed manner so that data privacy is ensured, with scalability and network performance improving [4]. With this, there is a dynamic trust scoring mechanism that evaluates each device's reliability in accordance with the behavioral patterns and history of interactions [5][6]. When combining FL with the trust scoring mechanism, the proposed solution would allow an IoT network to be autonomously able to identify anomalies and manage security. This system has shown practical applicability in such experiments in both the smart home and industrial environments to enhance IoT security, retain scalability, and maintain high privacy standards [7]. The adaptability of the system coupled with federated learning makes it apt for a variety of IoT ecosystems [8]
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Samuel Amoateng, Omolola A. Akinola, Victor Ogechukwu Anuebunwa, and Jesudunsin O. Olaobaju. "Designing a zero-trust post-quantum encryption framework for adaptive end-to-end network security in dynamic threat environments." World Journal of Advanced Engineering Technology and Sciences 13, no. 2 (2024): 934–48. https://doi.org/10.30574/wjaets.2024.13.2.0629.
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The advent of quantum computing poses a fundamental threat to classical encryption protocols, demanding urgent transformation in cybersecurity architectures. This study presents a U.S.-focused Zero-Trust Enabled Post-Quantum Encryption Framework (ZT-PQEF) designed to deliver adaptive end-to-end network security in dynamic threat environments. ZT-PQEF integrates NIST-standard post-quantum cryptographic algorithms (CRYSTALS-Kyber and Dilithium) with behavior-informed trust scoring, real-time key rotation, and telemetry-driven microsegmentation. A U.S. federal network simulation was used to benchmark the framework across nine performance metrics and seven critical system dimensions. Compared to conventional zero-trust and static PQC-enabled architectures, ZT-PQEF achieved a 22% improvement in cryptographic agility, reduced breach containment time by over 40%, and significantly lowered false-positive rates in behavioral anomaly detection. The framework preserved bandwidth viability and minimized resource overhead, confirming its suitability for high-throughput, resource-sensitive government deployments. These results demonstrate that ZT-PQEF delivers scalable, quantum-resilient, and policy-adaptive security, representing a critical advancement in post-quantum infrastructure protection and future-proof zero-trust implementation across the United States.
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Haridasan, Praveen Kotholliparambil. "The Salesforce Einstein Trust Layer for Retrieval-Augmented Generation (RAG) for Enterprise Applications." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–3. http://dx.doi.org/10.55041/ijsrem28465.
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Generative AI has the potential to revolutionize the enterprise workflows to great extent but it poses privacy, security, and data governance challenges. Companies who want to utilize advanced AI models like Large Language Models, are only allowed to do so under the guidelines of security and regulatory frameworks. Salesforce Einstein Trust Layer proposes a solution to these challenges by not only setting up a trusted layer for deploying Retrieval-Augmented Generation (RAG) models but also ensures that the data privacy standards are met while delivering the AI generated responses. This paper discusses how the Einstein Trust Layer facilitates the safe practical application of RAG in enterprise systems, including its general architecture, functionality, and the precise processes that demonstrate why the Einstein Trust Layer is a reliable means of incorporating LLMs into commercial processes. Keywords: Salesforce Einstein Trust Layer, Retrieval-Augmented Generation (RAG), Generative AI, Large Language Models (LLMs), Data Privacy, AI Governance, Enterprise AI, Data Masking, Toxicity Scoring, Dynamic Grounding, Zero-Data Retention, AI Compliance
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Anitha, 1. and Anirban Basu2. "NEURO-FUZZY SYSTEM BASED DYNAMIC RESOURCE ALLOCATION IN COLLABORATIVE CLOUD COMPUTING USING MULTI ATTRIBUTE QOS." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 6, February (2018): 01–14. https://doi.org/10.5281/zenodo.1413283.
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Cloud collaboration is an emerging technology which enables sharing of computer files using cloud computing. Here the cloud resources are assembled and cloud services are provided using these resources. Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud is challenging because resources offer different Quality of Service (QoS) and services running on these resources are risky for user demands. We propose a solution for resource allocation based on multi attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm perform better than others including power trust reputation based algorithms and harmony method which use single attribute to compute the reputation score of each resource allocated.
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Dasu, Lalitha Sravanti, Mannav Dhamija, Gurram Dishitha, Ajith Vivekanandan, and V. Sarasvathi. "Defending Against Identity Threats Using Risk-Based Authentication." Cybernetics and Information Technologies 23, no. 2 (2023): 105–23. http://dx.doi.org/10.2478/cait-2023-0016.
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Abstract Defending against identity-based threats, which have predominantly increased in the era of remote access and working, requires non-conventional, dynamic, intelligent, and strategic means of authenticating and authorizing. This paper aims at devising detailed risk-scoring algorithms for five real-time use cases to make identity security adaptive and risk-based. Zero-trust principles are incorporated by collecting sign-in logs and analyzing them continually to check for any anomalies, making it a dynamic approach. Users are categorized as risky and non-risky based on the calculated risk scores. While many adaptive security mechanisms have been proposed, they confine identities only to users. This paper also considers devices as having an identity and categorizes them as safe or unsafe devices. Further, results are displayed on a dashboard, making it easy for security administrators to analyze and make wise decisions like multifactor authentication, mitigation, or any other access control decisions as such.
Book chapters on the topic "Dynamic Trust Scoring"
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Lenart, Marcin, Andrzej Bielecki, Marie-Jeanne Lesot, Teodora Petrisor, and Adrien Revault d’Allonnes. "Dynamic Trust Scoring of Railway Sensor Information." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91262-2_51.
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Ishii, Akira, Yasuko Kawahata, and Nozomi Okano. "Significant Role of Trust and Distrust in Social Simulation." In The Psychology of Trust [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.101538.
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This paper introduces the Trust-Distrust Model and its applications, extending the Bounded Confidence Model, a theory of opinion dynamics, to include the relationship between trust and mistrust. In recent years, there has been an increase in the number of cases in which the prerequisites for conventional communication (e.g., the other person’s gender, appearance, tone of voice, etc.) cannot be established without the exchange of personal information. However, in recent years, there has been an increase in the use of personal information, such as letters and pictograms “as cryptographic asset data” for two-way communication. However, there are advantages and disadvantages to using information assets in the form of personalized data, which are excerpts of personal information as described above. In the future, the discussion of trust value in the above data will accelerate in indicators such as personal credit scoring. In this paper, the Trust-Distrust Model will be discussed with respect to theories that also address charismatic people, the effects of advertising, and social divisions. Furthermore, simulations of the Trust-Distrust Model show that 55% agreement is sufficient to build social consensus. By addressing this theory, we hope to use it to discuss and predict social risk in future credit scoring discussions.
Conference papers on the topic "Dynamic Trust Scoring"
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Fotis, Theofanis, Kitty Kioskli, and Eleni Seralidou. "Charting Trustworthiness: A Socio-Technical Perspective on AI and Human Factors." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006137.
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Integrating AI into critical decision-making environments, including cybersecurity, highlights the importance of understanding human factors in fostering trust and ensuring safe human-AI collaboration. Existing research emphasizes that personality traits, such as openness, trust propensity, and affinity for technology, significantly influence user interaction with AI systems, impacting trustworthiness and reliance behaviours. Furthermore, studies in cybersecurity underscore the socio-technical nature of threats, with human behaviour contributing to a significant portion of breaches. Addressing these insights, the study discusses the development and validation of a questionnaire designed to assess personality-driven factors in AI trustworthiness, advancing tools to mitigate human-centric risks in cybersecurity. Building on interdisciplinary foundations from cyberpsychology, human-computer interaction, and behavioural sciences, the questionnaire evaluates dimensions including ethical responsibility, collaboration, technical competence, and adaptability. Subject matter experts systematically reviewed items to ensure face and content validity, reflecting theoretical and empirical insights from prior studies on human behaviour and cybersecurity resilience. The tool’s scoring system employs weighted Likert-scale responses, enabling detailed evaluations of trust dynamics and identifying key areas for intervention. By bridging theoretical and applied perspectives, this research contributes to advancing the role of human factors in cybersecurity, offering actionable insights for the design of trustworthy AI systems and calibrated trust practices.
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Nastas, Natalia, Ecaterina Lungu, and Natalia Putin. "New impact in sports corruption." In The International Scientific Congress "Sports. Olimpysm. Health". SOH 2023. 8th Edition. The State University of Physical Education and Sport, 2025. https://doi.org/10.52449/soh23.39.
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Actuality. Corruption in sports refers to any unethical or illegal activities that compromise the integrity of sporting events, organizations, or athletes. It can take various forms, including match-fixing, doping, bribery, embezzlement, and other forms of dishonest behavior. Corruption in sports not only tarnishes the reputation of the games but also undermines the principles of fair competition and sportsmanship. Here are some key aspects and examples of corruption in sports: Match-Fixing: Match-fixing occurs when athletes, officials, or individuals involved in sports conspire to manipulate the outcome of a game or event. This can involve intentionally losing, scoring own goals, or ensuring that certain outcomes occur to benefit those involved in the fix, often for financial gain. Doping: Doping involves the use of banned substances or methods to enhance an athlete's performance. This practice is not only unethical but also poses health risks to athletes. Organizations like the World Anti-Doping Agency (WADA) work to combat doping in sports by implementing testing and regulations. Bribery and Corruption: Corruption can also manifest as bribery, where athletes or officials accept money or gifts to influence their decisions or actions in sports. This can range from bribing referees to influence match outcomes to paying off officials for favorable treatment. Embezzlement: Embezzlement occurs when individuals in positions of trust within sports organizations misappropriate funds for personal gain. This can involve stealing ticket revenue, sponsorship money, or other financial resources meant for the development of sports. Governance Issues: Sports organizations, including governing bodies and national associations, may become corrupt when leaders or officials engage in fraudulent practices, such as nepotism, misappropriation of funds, or election rigging. Gambling: The rise of sports betting can also contribute to corruption, as individuals may attempt to manipulate outcomes to profit from their bets. This can create incentives for match-fixing and other forms of cheating. The purpose of the research is to present a summary of the scientific literature on the threats to corruption in sport; introduce a framework to categorise these threats; identify research gaps in the field and provide safeguarding recommendations for sport organizations. Corruption in sports serves various purposes, although all of them are unethical and detrimental to the integrity of sports and fair competition. It's important to note that corruption in sports is not justifiable, and its purposes are harmful. Here are some of the purposes behind corruption in sports: Financial Gain: One of the primary purposes of corruption in sports is financial gain. Individuals involved in corrupt practices, such as match-fixing, bribery, or embezzlement, seek to profit from their actions. This can involve players, coaches, officials, or even organized crime syndicates looking to make money through illegal means. Influence and Power: Corruption can also be driven by the desire for influence and power within the sports world. Individuals may engage in corrupt activities to secure positions of authority, gain control over sports organizations, or manipulate the outcomes of events to their advantage. Preservation of Reputation: In some cases, athletes or teams may resort to corrupt practices to protect or enhance their reputation. This can involve doping to achieve better results and maintain a positive public image, even if it means cheating. Maintaining Employment: Athletes, coaches, and other individuals involved in sports may engage in corrupt practices to ensure their continued employment or contract extensions. This can include actions such as point-shaving in team sports to influence the final score without drawing suspicion. Research into corruption in sports typically employs various methods to gather data, analyze trends, and uncover the underlying causes and consequences of corrupt practices. These research methods can be broadly categorized into quantitative and qualitative approaches, and they often involve a combination of techniques. Here are some common methods used in researching corruption in sports: Surveys and Questionnaires: surveys or questionnaires to collect data from athletes, coaches, officials, and other stakeholders in the sports world. These surveys can ask about personal experiences with corruption, perceptions of corruption within the sport, and attitudes toward anti-corruption measures. Interviews: In-depth interviews with key informants, such as athletes, coaches, sports officials, and experts, can provide valuable qualitative data. Interviews allow researchers to explore the motivations, behaviors, and perspectives of individuals involved in or affected by corruption in sports. Content Analysis: Researchers analyze written, audiovisual, or digital content related to sports, such as news articles, social media discussions, and legal documents. Content analysis can help identify and track instances of corruption, as well as public reactions and perceptions. Case Studies: Researchers often conduct detailed case studies of specific instances of corruption in sports. This qualitative approach involves in-depth examination of the circumstances, individuals involved, and consequences of particular corruption cases. Data Mining: With the proliferation of digital data, researchers can use data mining techniques to extract valuable insights from vast datasets related to sports, including financial transactions, betting patterns, and social media interactions. Data mining can help detect irregularities and suspicious activities. Observation: Ethnographic research involves observing and immersing oneself in the sports environment to gain firsthand insights into the culture, practices, and dynamics related to corruption. Researchers may attend sporting events, interact with participants, and document their observations. Secondary Data Analysis: Researchers often analyze existing datasets and reports related to sports corruption. This can include data from sports governing bodies, law enforcement agencies, and academic studies. Secondary data analysis allows for the examination of trends and patterns over time. Surveillance and Whistleblower Reports: Surveillance methods and whistleblower reports can be valuable sources of information on corrupt activities in sports. These reports can include evidence of match-fixing, doping, and other illicit activities. Network Analysis: Researchers use network analysis techniques to map out relationships and connections among individuals and organizations involved in corruption in sports. This approach helps identify key actors and their roles in corrupt networks. Comparative Studies: Comparative research involves analyzing corruption in sports across different countries, regions, or sports disciplines. This approach allows researchers to identify variations in corrupt practices and anti-corruption efforts. Research findings and results in the area of corruption in sports have revealed numerous insights into the extent, causes, consequences, and strategies to combat corruption within the sports industry. While specific findings can vary depending on the research focus and methodology, here are some common themes and key findings from studies on corruption in sports: Prevalence of Corruption: Research consistently highlights that corruption in sports is a widespread problem that affects various sports disciplines and regions across the world. Studies have documented cases of match-fixing, doping, bribery, and embezzlement in both amateur and professional sports. Financial Impact: Corruption in sports can have significant financial consequences. Research has shown that match-fixing and doping scandals can lead to financial losses for sports organizations, sponsors, and broadcasters. Additionally, corruption can deter legitimate investment in sports. Motivations: Studies often explore the motivations behind corrupt practices in sports. Financial gain, the pursuit of power and influence, and a desire for success or recognition are among the primary motivations identified for individuals involved in corruption in sports. In conclusion, corruption in sports represents a significant and multifaceted challenge that has far-reaching consequences for athletes, organizations, fans, and the integrity of sports as a whole. The findings and research in this area have shed light on the prevalence, motivations, and impacts of corruption in various sports disciplines around the world. Corruption in sports encompasses practices such as match-fixing, doping, bribery, embezzlement, and more, with financial gain, power, and influence often serving as key motivations. It is not limited to a particular sport or region, and it can undermine the very essence of fair competition, sportsmanship, and trust that underpin the sporting world. Efforts to combat corruption in sports involve a combination of stringent regulations, effective monitoring and enforcement, education and awareness programs, whistleblower protection, and enhanced transparency and governance within sports organizations. Technology also plays a pivotal role in both detecting and perpetrating corruption, presenting both challenges and opportunities for anti-corruption efforts. Preserving the integrity of sports is essential not only for the well-being of athletes but also for maintaining the trust and enthusiasm of fans and sponsors. Corruption erodes the core values of sports and can lead to financial losses, damaged reputations, and a decline in fan engagement.