An Alert Fusion Method Based on Grey Relation and Attribute Similarity Correlation

Liang, Wei; Chen, Zuo; Wen, Ya; Xiao, Weidong

doi:10.3991/ijoe.v12i08.5958

Cited by 2 publications

(6 citation statements)

References 13 publications

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“…These three methods have become representative methods in recent years. They are the no-weight method, scheme 1 [ 29 ] and scheme 2 [ 31 ], which are different from our method in terms of the attribute weight determination and aggregation method. The no-weight method assigns the same weight to each attribute, and its aggregation method is the same as ours.…”

Section: Methodsmentioning

confidence: 99%

“…The no-weight method assigns the same weight to each attribute, and its aggregation method is the same as ours. Scheme 1 [ 29 ] and scheme 2 [ 31 ] adopt different attribute weighting methods and aggregation methods. Therefore, these three methods and our method have comparative significance.…”

Section: Methodsmentioning

confidence: 99%

“…All of the schemes in Table 4 have a similar consideration of the alert’s feature quantity and they exclude the irrelevant attribute. Our scheme and the scheme in [ 31 ] both employ a classification-based approach to reduce duplicate alerts, while the scheme in [ 29 ] does not consider this aspect. The ratios of the four schemes are approximate for the aggregation rate.…”

Section: Methodsmentioning

confidence: 99%

“…The ratios of the four schemes are approximate for the aggregation rate. Overall, our scheme is ahead of the no-weight method approach and scheme 1 [ 29 ], but slightly lower than scheme 2 [ 31 ], in terms of its reduction efficiency. However, we should also consider the integrity of the information in addition to the aggregation rate.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the determination of attribute weights is a key issue when using the attribute similarity method for alert aggregation. Wei et al [ 29 ] used gray correlation analysis to determine the importance of alert attributes in classification and then used it as the weight of attributes. However, they assign the same attribute weight to all attack types, while ignoring that the attribute weight might be different, depending on the attack scenario.…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity

Sun

Chen

2020

Entropy

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With the emergence of network security issues, various security devices that generate a large number of logs and alerts are widely used. This paper proposes an alert aggregation scheme that is based on conditional rough entropy and knowledge granularity to solve the problem of repetitive and redundant alert information in network security devices. Firstly, we use conditional rough entropy and knowledge granularity to determine the attribute weights. This method can determine the different important attributes and their weights for different types of attacks. We can calculate the similarity value of two alerts by weighting based on the results of attribute weighting. Subsequently, the sliding time window method is used to aggregate the alerts whose similarity value is larger than a threshold, which is set to reduce the redundant alerts. Finally, the proposed scheme is applied to the CIC-IDS 2018 dataset and the DARPA 98 dataset. The experimental results show that this method can effectively reduce the redundant alerts and improve the efficiency of data processing, thus providing accurate and concise data for the next stage of alert fusion and analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity

Sun

Chen

2020

Entropy

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A New Fault Diagnosis Method for Quality Control of Electromagnet Based on T–S Fault Tree and Grey Relation

Pang

Dai

Zhou

et al. 2022

Int. J. Rel. Qual. Saf. Eng.

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Product fault diagnosis has been a focus of research in the field of quality control. Many studies have been made to ensure the accuracy of fault diagnosis in electromagnet quality control direction. However, in the pursuit of perfect identification method, the fault diagnosis process is extremely complex in quality control management. A new fault diagnosis method for quality control of electromagnet machining processes based on [Formula: see text]–[Formula: see text] fault tree and grey relation degree analysis is proposed in this paper. First, [Formula: see text]–[Formula: see text] fault tree is used to analyze and describe the dynamic fault relationship of the electromagnet machining processes. In order to make the fault mode more intuitive and specific, [Formula: see text]–[Formula: see text] fault tree logic diagram is used to describe the actual fault form of machining processes. Second, according to the fault output algorithm of [Formula: see text]–[Formula: see text] fault tree, the fault rate of basic event in each cycle is calculated and the fault rate of intermediate event and top event in each cycle is obtained. Then, the grey relation method is applied to calculate the relation between the basic event and the trend of top event failure rates in electromagnet machining processes. Based on the obtained relation degree, the faulty system of electromagnet machining processes can be repaired pertinently and achieve the purpose of quality control. Finally, an example of electromagnet quality control’s fault diagnosis is used to verify the objectivity and simplicity of the new method.

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An Alert Fusion Method Based on Grey Relation and Attribute Similarity Correlation

Cited by 2 publications

References 13 publications

An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity

An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity

A New Fault Diagnosis Method for Quality Control of Electromagnet Based on T–S Fault Tree and Grey Relation

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