Secure Resource Management in Cloud Computing: Challenges, Strategies and Meta-Analysis
- URL: http://arxiv.org/abs/2502.03149v1
- Date: Wed, 05 Feb 2025 13:20:35 GMT
- Title: Secure Resource Management in Cloud Computing: Challenges, Strategies and Meta-Analysis
- Authors: Deepika Saxena, Smruti Rekha Swain, Jatinder Kumar, Sakshi Patni, Kishu Gupta, Ashutosh Kumar Singh, Volker Lindenstruth,
- Abstract summary: This paper examines the cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management.<n>The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies.<n>The study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management.
- Score: 2.9395329090330957
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This paper provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, the paper discusses the trade-offs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and secure resource management. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management.
Related papers
- Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach [8.119190256503433]
Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards.
reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments.
This survey highlights the potential of RL to address the challenges of selfish mining, including protocol design, threat detection, and security analysis.
arXiv Detail & Related papers (2025-02-24T16:42:51Z) - A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments [55.60375624503877]
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data.
This survey is motivated by the urgent need to understand how the unique characteristics of cloud, edge, and federated deployments shape attack vectors and defense requirements.
We systematically examine the evolution of attack methodologies and defense mechanisms across these environments, demonstrating how environmental factors influence security strategies in critical sectors such as autonomous vehicles, healthcare, and financial services.
arXiv Detail & Related papers (2025-02-22T03:46:50Z) - A Survey on Vulnerability Prioritization: Taxonomy, Metrics, and Research Challenges [20.407534993667607]
Resource constraints necessitate effective vulnerability prioritization strategies.
This paper introduces a novel taxonomy that categorizes metrics into severity, exploitability, contextual factors, predictive indicators, and aggregation methods.
arXiv Detail & Related papers (2025-02-16T10:33:37Z) - Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training.
Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs.
This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
arXiv Detail & Related papers (2024-11-15T08:09:28Z) - A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities [4.589028594967462]
We provide an in-depth analysis of various attack vectors, focusing on new attack surfaces introduced by advanced components in smart grids.
Following this, we examine innovative detection and mitigation strategies, including game theory, graph theory, and machine learning.
We first discuss the research opportunities for existing and emerging strategies, and then explore the potential role of new techniques.
arXiv Detail & Related papers (2024-07-10T18:03:24Z) - Threat-Informed Cyber Resilience Index: A Probabilistic Quantitative Approach to Measure Defence Effectiveness Against Cyber Attacks [0.36832029288386137]
This paper introduces the Cyber Resilience Index (CRI), a threat-informed probabilistic approach to quantifying an organisation's defence effectiveness against cyber-attacks (campaigns)
Building upon the Threat-Intelligence Based Security Assessment (TIBSA) methodology, we present a mathematical model that translates complex threat intelligence into an actionable, unified metric similar to a stock market index, that executives can understand and interact with while teams can act upon.
arXiv Detail & Related papers (2024-06-27T17:51:48Z) - QBER: Quantifying Cyber Risks for Strategic Decisions [0.0]
We introduce QBER approach to offer decision-makers measurable risk metrics.
The QBER evaluates losses from cyberattacks, performs detailed risk analyses based on existing cybersecurity measures, and provides thorough cost assessments.
Our contributions involve outlining cyberattack probabilities and risks, identifying Technical, Economic, and Legal (TEL) impacts, creating a model to gauge impacts, suggesting risk mitigation strategies, and examining trends and challenges in implementing widespread Cyber Risk Quantification (CRQ)
arXiv Detail & Related papers (2024-05-06T14:25:58Z) - LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models.
It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
arXiv Detail & Related papers (2024-04-01T16:50:54Z) - Navigating Cybersecurity Training: A Comprehensive Review [7.731471533663403]
This survey examines a spectrum of cybersecurity awareness training methods, analyzing traditional, technology-based, and innovative strategies.
It evaluates the principles, efficacy, and constraints of each method, presenting a comparative analysis that highlights their pros and cons.
arXiv Detail & Related papers (2024-01-20T21:14:24Z) - On strategies for risk management and decision making under uncertainty shared across multiple fields [55.2480439325792]
The paper finds more than 110 examples of such strategies and this approach to risk is termed RDOT: Risk-reducing Design and Operations Toolkit.
RDOT strategies fall into six broad categories: structural, reactive, formal, adversarial, multi-stage and positive.
Overall, RDOT represents an overlooked class of versatile responses to uncertainty.
arXiv Detail & Related papers (2023-09-06T16:14:32Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A
Contemporary Survey [114.17568992164303]
Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention.
This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques.
New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
arXiv Detail & Related papers (2023-03-11T04:19:31Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
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.