A new BRB model for security-state assessment of cloud computing based on the impact of external and internal environments

Hang, Wei; Hu, Guan Yu; Zhou, Zhi Jie; Qiao, Pei Li; Zhou, Zhen; Zhang, Youmin

doi:10.1016/j.cose.2017.11.003

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…3) Evolutionary computing: A very recent approach to forecast network security situation is based on belief rule base (BRB) models and evolutionary algorithms, namely CMA-ES. This approach emerged in 2016, and was since then described and continuously improved by Hu et al [101], [102] and Wei et al [103], including the improvements in network security situation assessment [107]. BRB model includes a series of belief rules and can be built from expert knowledge as well as historical data.…”

Section: Other Approachesmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

214

110

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This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation.

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Section: Other Approachesmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

214

110

View full text Add to dashboard Cite

show abstract

“…Generally, a rule-based system uses rules as its knowledge representation scheme, and uses a reasoning approach to infer the result of queries by activating and aggregating rules. The belief rule-based (BRB) system is a kind of rule-based system that embeds belief degrees in the consequent term of each rule, and it has been widely applied in many fields [2]- [4]. It can handle both quantitative and qualitative information, and is considered to be more interpretable than deep-learning-based tools.…”

Section: Introductionmentioning

confidence: 99%

A Balance Adjusting Approach of Extended Belief-Rule-Based System for Imbalanced Classification Problem

et al. 2020

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The extended belief-rule-based (EBRB) system has become a widely recognized and effective rule-based system in decision-making. The system uses a data-driven method to generate the rule base by transforming each training sample into a rule. Hence, when an EBRB system is applied in an imbalanced classification dataset, the imbalance of training dataset will retain in the generated rule base. More specifically, the number of rules transformed from majority classes will be far greater than the rules transformed from minority classes. This issue usually leads to a sharp decrease in the accuracies of minority classes. This study analyses how the imbalance of training dataset exists in the generated EBRB and then proposes a Balance Adjusting (BA) approach to eliminate the influence of imbalance in the rule base. The BA approach adjusts rule activation weights of all activated rules, and further enhances the competitiveness of rules with higher activation weight during the rule aggregation process of the EBRB system. Several case studies in imbalanced benchmark classification datasets from UCI demonstrate how the use of the BA approach improves the performance of the EBRB system. This study also conducts a series of experiments to validate the improvement of the proposed approach compared with some conventional and recent existing works. The comparison results illustrate that the BA approach is feasible, effective and robust, and it performs well especially in large scale datasets. Moreover, the BA approach can also combine with various rule activation weight calculation methods, which means it might worth to be applied as a generic process before the rule aggregation process of the EBRB system. INDEX TERMS Extended belief-rule-based system, rule activation weight, imbalanced classification problem.

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