Data provenance for cloud forensic investigations, security, challenges, solutions and future perspectives: A survey

Abiodun, Oludare Isaac; Alawida, Moatsum; Omolara, Abiodun Esther; Alabdulatif, Abdulatif

doi:10.1016/j.jksuci.2022.10.018

Cited by 28 publications

(16 citation statements)

References 149 publications

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“…Provenance models are widely adopted in various research areas as the data representation model for build assessments about data reliability and quality [ 39 , 40 , 41 , 42 ]. However, because the existing provenance models focus on data or are specialized for specific domains, it is difficult to apply them to other domains that were not initially targeted [ 43 , 44 , 45 ]. Therefore, in this research, we define a provenance meta-model, which is a general model that can be adapted for a specific target domain.…”

Section: Proposed Approachmentioning

confidence: 99%

Provenance-Based Trust-Aware Requirements Engineering Framework for Self-Adaptive Systems

Lee

2023

Sensors

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With the development of artificial intelligence technology, systems that can actively adapt to their surroundings and cooperate with other systems have become increasingly important. One of the most important factors to consider during the process of cooperation among systems is trust. Trust is a social concept that assumes that cooperation with an object will produce positive results in the direction we intend. Our objectives are to propose a method for defining trust during the requirements engineering phase in the process of developing self-adaptive systems and to define the trust evidence models required to evaluate the defined trust at runtime. To achieve this objective, we propose in this study a provenance-based trust-aware requirement engineering framework for self-adaptive systems. The framework helps system engineers derive the user’s requirements as a trust-aware goal model through analysis of the trust concept in the requirements engineering process. We also propose a provenance-based trust evidence model to evaluate trust and provide a method for defining this model for the target domain. Through the proposed framework, a system engineer can treat trust as a factor emerging from the requirements engineering phase for the self-adaptive system and understand the factors affecting trust using the standardized format.

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Section: Proposed Approachmentioning

confidence: 99%

Provenance-Based Trust-Aware Requirements Engineering Framework for Self-Adaptive Systems

Lee

2023

Sensors

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“…Creating and testing a customized incident response plan that is adapted to the specifics of cloud environments is crucial in order to address this [74], [75]. Close cooperation with the cloud provider is essential to a smooth incident investigation and resolution process, guaranteeing a planned and efficient method for managing security breaches and reducing possible harm [76], [77].…”

Section: Figure 7 Cloud Incident Response and Forensicsmentioning

confidence: 99%

Security challenges in cloud computing: A comprehensive analysis

Janet Julia Ang'udi

2023

World J. Adv. Eng. Technol. Sci.

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The security issues surrounding cloud computing, a quickly developing technology that is now essential to both personal and business computing, are thoroughly examined in this study. Cloud computing presents serious security risks that require careful attention, despite its many advantages such as scalability, cost-effectiveness, and flexibility. In-depth discussions of a number of important security topics are covered in this paper, including network security, access control, data breaches, legal and regulatory framework compliance, and new threats and vulnerabilities. The paper illuminates the complexities of data and application security in the cloud environment by thoroughly examining these subjects. The study also looks at actual case studies to show how security breaches in cloud computing affect things and how to fix them. The study intends to offer insightful analyses of security challenges with practical implications by examining these cases and providing lessons learned from incidents that have happened in various cloud computing scenarios. Our comprehension of the complex nature of security threats and the tactics used to counter and mitigate them is improved by looking closely at these cases. This study looks into the potential benefits of cloud security enhancements from emerging technologies like artificial intelligence and machine learning, in addition to identifying existing challenges. The research investigates the potential benefits of these technologies in terms of automated security responses, adaptive access controls, and proactive threat detection. Cloud environments may be able to improve their security posture against new and sophisticated threats by utilizing the powers of AI and machine learning. Furthermore, by projecting future trends and challenges in cloud security, this paper offers a forward-looking view of the changing field. Comprehending these possible obstacles is crucial to creating proactive and flexible security approaches that can successfully tackle the ever-changing landscape of cloud computing. This paper's ultimate objective is to offer insightful information about practical approaches and industry best practices for safeguarding cloud environments. Organizations can navigate the complexity of the cloud landscape while protecting their data and applications from the ever-evolving threat landscape by striking a balance between the enormous potential of cloud computing and the necessity of maintaining strong security measures.

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“…The procedures for proving the absence of vulnerabilities in software applications is a challenging task since it is nearly impossible to guarantee complete absence [176]. However, there are several techniques and practices that can help increase confidence in the security of a system.…”

Section: Techniques To Prove the Absence Of Vulnerabilitiesmentioning

confidence: 99%

Theory and practice in secure software development lifecycle: A comprehensive survey

Otieno¹,

Odera²,

Ounza³

2023

World J. Adv. Res. Rev.

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Software development security refers to the practice of integrating security measures and considerations throughout the software development lifecycle to ensure the confidentiality, integrity, and availability of software systems. It involves identifying, mitigating, and eliminating security vulnerabilities and threats that could be exploited by attackers. The goal of this paper is to survey the various concepts and methodologies directed towards software security, and the identification of any missing gaps. Based on the findings, it is noted that the development of secure software requires a proactive and comprehensive approach. It begins with establishing secure design principles and incorporating security requirements from the initial stages of development. Here, secure coding practices, such as input validation, output encoding, and secure authentication and authorization mechanisms, are employed to prevent common security vulnerabilities. In addition, regular security testing, including penetration testing and vulnerability scanning, helps identify and address potential weaknesses in the software. Normally, code reviews and security audits are conducted to ensure adherence to secure coding practices and identify any security flaws. It is important that security training and awareness programs be provided to developers and other stakeholders to foster a security-conscious culture. To minimize potential vulnerabilities, secure configuration management, which involves properly configuring servers, networks, and dependencies may be utilized. On the other hand, regular updates and patching are essential to address known security vulnerabilities in software components. To guide their software development security practices, organizations may follow established security standards and frameworks such as ISO 27001 or NIST Cybersecurity Framework. By prioritizing software development security, organizations can protect sensitive data, prevent unauthorized access, and mitigate the risk of security breaches and incidents. In the long run, this helps build trust with users and stakeholders, enhances the reputation of the software, and reduces the potential impact of security incidents on the organization.

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Data provenance for cloud forensic investigations, security, challenges, solutions and future perspectives: A survey

Cited by 28 publications

References 149 publications

Provenance-Based Trust-Aware Requirements Engineering Framework for Self-Adaptive Systems

Provenance-Based Trust-Aware Requirements Engineering Framework for Self-Adaptive Systems

Security challenges in cloud computing: A comprehensive analysis

Theory and practice in secure software development lifecycle: A comprehensive survey

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