Access control technologies for Big Data management systems: literature review and future trends

Colombo, Pietro; Ferrari, Elena

doi:10.1186/s42400-018-0020-9

Cited by 38 publications

(23 citation statements)

References 63 publications

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“…Although, association rule learning techniques produce rules from data, however, there is a problem of redundancy generation [153] that makes the policy rule-set complex. Therefore, understanding such problems in policy rule generation and effectively handling such problems using existing algorithms or newly proposed algorithm for a particular problem domain like access control [165] is needed, which could be another research issue in cybersecurity data science. • Hybrid learning method : Most commercial products in the cybersecurity domain contain signature-based intrusion detection techniques [41] or a combination of deep learning and machine-learning methods can be used to extract the target insight for a particular problem domain like intrusion detection, malware analysis, access control, etc.…”

Section: Research Issues and Future Directionsmentioning

confidence: 99%

Cybersecurity Data Science: An Overview from Machine Learning Perspective

Sarker¹

2020

Preprint

111

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In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

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Section: Research Issues and Future Directionsmentioning

confidence: 99%

Cybersecurity Data Science: An Overview from Machine Learning Perspective

Sarker¹

2020

Preprint

111

View full text Add to dashboard Cite

show abstract

“…Although, association rule learning techniques produce rules from data, however, there is a problem of redundancy generation [153] that makes the policy rule-set complex. Therefore, understanding such problems in policy rule generation and effectively handling such problems using existing algorithms or newly proposed algorithm for a particular problem domain like access control [165] is needed, which could be another research issue in cybersecurity data science. • Hybrid learning method: Most commercial products in the cybersecurity domain contain signature-based intrusion detection techniques [41].…”

Section: Research Issues and Future Directionsmentioning

confidence: 99%

Cybersecurity data science: an overview from machine learning perspective

et al. 2020

View full text Add to dashboard Cite

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

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“…In particular, we focus on the CapBAC model. Despite the drawback of low context-awareness as pointed out by the authors of [9,27], the CapBAC model can ensure the critical principle of least privilege, i.e., each subject uses the least amount of privilege (i.e., access rights) necessary to finish its job. In addition, the CapBAC model allows subjects to delegate access rights from one to another for flexible and spontaneous access control.…”

Section: Research Objectivementioning

confidence: 99%

Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things

Nakamura

Zhang

Sasabe

et al. 2020

Sensors

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Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what conditions, has been recognized as an effective solution to address this issue. To cope with the distributed and trust-less nature of IoT systems, we propose a decentralized and trustworthy Capability-Based Access Control (CapBAC) scheme by using the Ethereum smart contract technology. In this scheme, a smart contract is created for each object to store and manage the capability tokens (i.e., data structures recording granted access rights) assigned to the related subjects, and also to verify the ownership and validity of the tokens for access control. Different from previous schemes which manage the tokens in units of subjects, i.e., one token per subject, our scheme manages the tokens in units of access rights or actions, i.e., one token per action. Such novel management achieves more fine-grained and flexible capability delegation and also ensures the consistency between the delegation information and the information stored in the tokens. We implemented the proposed CapBAC scheme in a locally constructed Ethereum blockchain network to demonstrate its feasibility. In addition, we measured the monetary cost of our scheme in terms of gas consumption to compare our scheme with the existing Blockchain-Enabled Decentralized Capability-Based Access Control (BlendCAC) scheme proposed by other researchers. The experimental results show that the proposed scheme outperforms the BlendCAC scheme in terms of the flexibility, granularity, and consistency of capability delegation at almost the same monetary cost.

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Access control technologies for Big Data management systems: literature review and future trends

Cited by 38 publications

References 63 publications

Cybersecurity Data Science: An Overview from Machine Learning Perspective

Cybersecurity Data Science: An Overview from Machine Learning Perspective

Cybersecurity data science: an overview from machine learning perspective

Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things

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