Zero Trust: Applications, Challenges, and Opportunities
- URL: http://arxiv.org/abs/2309.03582v1
- Date: Thu, 7 Sep 2023 09:23:13 GMT
- Title: Zero Trust: Applications, Challenges, and Opportunities
- Authors: Saeid Ghasemshirazi, Ghazaleh Shirvani, Mohammad Ali Alipour,
- Abstract summary: This survey comprehensively explores the theoretical foundations, practical implementations, applications, challenges, and future trends of Zero Trust.
We highlight the relevance of Zero Trust in securing cloud environments, facilitating remote work, and protecting the Internet of Things (IoT) ecosystem.
Integrating Zero Trust with emerging technologies like AI and machine learning augments its efficacy, promising a dynamic and responsive security landscape.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The escalating complexity of cybersecurity threats necessitates innovative approaches to safeguard digital assets and sensitive information. The Zero Trust paradigm offers a transformative solution by challenging conventional security models and emphasizing continuous verification and least privilege access. This survey comprehensively explores the theoretical foundations, practical implementations, applications, challenges, and future trends of Zero Trust. Through meticulous analysis, we highlight the relevance of Zero Trust in securing cloud environments, facilitating remote work, and protecting the Internet of Things (IoT) ecosystem. While cultural barriers and technical complexities present challenges, their mitigation unlocks Zero Trust's potential. Integrating Zero Trust with emerging technologies like AI and machine learning augments its efficacy, promising a dynamic and responsive security landscape. Embracing Zero Trust empowers organizations to navigate the ever-evolving cybersecurity realm with resilience and adaptability, redefining trust in the digital age.
Related papers
- Authentication and identity management based on zero trust security model in micro-cloud environment [0.0]
The Zero Trust framework can better track and block external attackers while limiting security breaches resulting from insider attacks in the cloud paradigm.
This paper focuses on authentication mechanisms, calculation of trust score, and generation of policies in order to establish required access control to resources.
arXiv Detail & Related papers (2024-10-29T09:06:13Z) - Enhancing Enterprise Security with Zero Trust Architecture [0.0]
Zero Trust Architecture (ZTA) represents a transformative approach to modern cybersecurity.
ZTA shifts the security paradigm by assuming that no user, device, or system can be trusted by default.
This paper explores the key components of ZTA, such as identity and access management (IAM), micro-segmentation, continuous monitoring, and behavioral analytics.
arXiv Detail & Related papers (2024-10-23T21:53:16Z) - Cross-Modality Safety Alignment [73.8765529028288]
We introduce a novel safety alignment challenge called Safe Inputs but Unsafe Output (SIUO) to evaluate cross-modality safety alignment.
To empirically investigate this problem, we developed the SIUO, a cross-modality benchmark encompassing 9 critical safety domains, such as self-harm, illegal activities, and privacy violations.
Our findings reveal substantial safety vulnerabilities in both closed- and open-source LVLMs, underscoring the inadequacy of current models to reliably interpret and respond to complex, real-world scenarios.
arXiv Detail & Related papers (2024-06-21T16:14:15Z) - Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI [0.0]
A key area in AI-based cybersecurity focuses on defending deep neural networks against malicious perturbations.
We attempt to validate results from prior work on certified robustness using the VeriGauge toolkit.
Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation.
arXiv Detail & Related papers (2024-05-29T04:37:19Z) - GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints.
Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
arXiv Detail & Related papers (2024-05-20T14:43:46Z) - Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks [2.28438857884398]
Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges.
This study proposes an innovative security framework inspired by Control-Flow (CFA) mechanisms, traditionally used in cybersecurity.
We authenticate and verify the integrity of model updates across the network, effectively mitigating risks associated with model poisoning and adversarial interference.
arXiv Detail & Related papers (2024-03-15T04:03:34Z) - A Zero Trust Framework for Realization and Defense Against Generative AI
Attacks in Power Grid [62.91192307098067]
This paper proposes a novel zero trust framework for a power grid supply chain (PGSC)
It facilitates early detection of potential GenAI-driven attack vectors, assessment of tail risk-based stability measures, and mitigation of such threats.
Experimental results show that the proposed zero trust framework achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.
arXiv Detail & Related papers (2024-03-11T02:47:21Z) - Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Zero Trust for Cyber Resilience [13.343937277604892]
This chapter draws attention to the cyber resilience within the zero-trust model.
We introduce the evolution from traditional perimeter-based security to zero trust and discuss their difference.
arXiv Detail & Related papers (2023-12-05T16:53:20Z) - A Survey of Trustworthy Federated Learning with Perspectives on
Security, Robustness, and Privacy [47.89042524852868]
Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios.
However, challenges around data isolation and privacy threaten the trustworthiness of FL systems.
arXiv Detail & Related papers (2023-02-21T12:52:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.