Trust Evaluation of Public Cloud Service Providers Using Genetic Algorithm with Intelligent Rules

Jaithunbi, A. K.; Sabena, S.; SaiRamesh, L.

doi:10.1007/s11277-021-08876-4

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…The trusted tasks are offloaded securely among the mobile clouds. The researchers in [35], proposed machine learning-based trust evaluation model for e-commerce platforms. Quantitative trust computing alters the qualitative trust computation model by adopting machine learning prediction of behavior methods.…”

Section: Resultsmentioning

confidence: 99%

“…Table 3 represents the existing works in cloud computing security. [31] Biometric authentication Cloud data security  Encryption techniques were very simple and easy to break [32] Machine learning-based mutual authentication method Secure data transmission  High complexity and easy to impersonate [33] MCDM and BWM methods Trusted CSP selection  Increased SLA violation [34] Genetic algorithm based security Trusted CSP selection  Less Convergence and not enough parameters for trusted CSP selection [35] Fundamental trust management approach Secure task scheduling  Lack of communication security [36] Machine learning based trust evaluation Malicious behavior prediction  Poor QoS [37] Trust evaluation and reputation CSP selection  Poor credibility [38] Trust-based big data scheduling Secure task scheduling  Highly time intensive [39] Secure Secure cloud computing is performed to increase the data security and performance of the application with high quality of service (QoS). However, the QoS of the applications reduces due to various issues regarding complexity, latency, insufficient security, etc.…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Literature Review on Security Issues in Cloud Computing Using Edge Computing and Blockchain: Threat, Mitigation, and Future Trends

Ahmad Khan,

Mahmood Khan,

(Corresponding Author)

2023

MJCS

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This Systematic Literature Review (SLR) examines the security threats experienced by cloud computing along with QoS by edge computing and scheduling. Various consumers and cloud service providers (CSP) obtained benefits from the cloud computing environment. However, various privacy and security challenges occur in this digital age. This SLR mainly aims to review the state of the art research studies on secure cloud computing with high QoS and its challenges. We examined numerous research studies related to cloud computing security with secure service level agreement (SLA) which are published between 2019 to 2022 from the reputed electronic databases. Cloud computing security is ensured mostly based on trust evaluation, implementation of blockchain technology, authentication, and cryptography techniques, and implementing edge computing in the cloud environment. This SLR also analyzed the research gaps of the previous research papers and provides future research directions to obtain secure cloud computing environment with high QoS performance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Systematic Literature Review on Security Issues in Cloud Computing Using Edge Computing and Blockchain: Threat, Mitigation, and Future Trends

Ahmad Khan,

Mahmood Khan,

(Corresponding Author)

2023

MJCS

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“…One of the main issues, which has significantly reduced the adoption of IoT devices, is mainly security issues [28]. The work [29] illustrated that a wide array of tools are capable of helping in the mitigation of cyber threats, which mainly target IoT systems.…”

Section: Related Workmentioning

confidence: 99%

Distribution Diversity Method of Feature Optimization (DDMFO) to Defend the Intrusion Practices on IoT Networks

Bhargavi

Yellamma

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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The new forms of networks labeled IoT are relatively new and which become buzz in this decade. The network architecture lets any smart device loosely connect to the Internet under internet protocol. However, the other dimension of this network facilitates intruders to access the network with no critical efforts. The context of intrusions has been delineated as intrusion practices of other devices connected to an IoT network that are connected to external networks through a gateway. Vice versa, the compromised IoT network intends to communicate with external devices or networks to perform intrusion practices. In this regard, intrusion detection through machine learning demands significant feature selection and optimization techniques. This manuscript endeavored to demonstrate the scope distribution diversity assessment methods of traditional statistical practices toward feature selection and optimization in this regard, the contribution “Distribution Diversity Method of Feature Optimization (DDMFO) to Protect Intrusion Practices on IoT Networks” of this paper uses the Dice Similarity Coefficient procedure to pick the optimum characteristics for the training of the classifier. The classifier that has been adopted in this contribution is Naïve Bayes, trained by the features selected by the proposal. The experimental research concludes the significance of the taxonomy, which demonstrates substantial accuracy and minimal false alarm.

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“…Building these provider reputation systems and deciding the weights to be assigned to each evaluator's score is beyond the scope of this paper. However, it seems essential to include the possibility of taking into account this type of reputation system in the PAdv since in complex contexts involving a multitude of providers such as Cloud Computing [48] or IoT [49,50] (both similar to identity federations in terms of distribution and heterogeneity) it is a trend that is gaining more and more strength [51], even recurring to decentralised reputation systems based on blockchain [52].…”

Section: Reputationmentioning

confidence: 99%

User-Centric Privacy for Identity Federations Based on a Recommendation System

Villarán

Beltrán

2022

Electronics

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Specifications such as SAML, OAuth, OpenID Connect and Mobile Connect are essential for solving identification, authentication and authorisation in contexts such as mobile apps, social networks, e-commerce, cloud computing or the Internet of Things. However, end-users relying on identity providers to access resources, applications or services lose control over the Personally Identifiable Information (PII) they share with the different providers composing identity federations. This work proposes a user-centric approach based on a recommendation system to support users in making privacy decisions such as selecting service providers or choosing their privacy settings. The proposed Privacy Advisor gives end-users privacy protection by providing personalised recommendations without compromising the identity federations’ functionalities or requiring any changes in their underlying specifications. A proof of concept of the proposed recommendation system is presented to validate and evaluate its utility and feasibility.

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Trust Evaluation of Public Cloud Service Providers Using Genetic Algorithm with Intelligent Rules

Cited by 11 publications

References 10 publications

A Systematic Literature Review on Security Issues in Cloud Computing Using Edge Computing and Blockchain: Threat, Mitigation, and Future Trends

A Systematic Literature Review on Security Issues in Cloud Computing Using Edge Computing and Blockchain: Threat, Mitigation, and Future Trends

Distribution Diversity Method of Feature Optimization (DDMFO) to Defend the Intrusion Practices on IoT Networks

User-Centric Privacy for Identity Federations Based on a Recommendation System

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