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2022-02-07
Liu, Jin-zhou.  2021.  Research on Network Big Data Security Integration Algorithm Based on Machine Learning. 2021 International Conference of Social Computing and Digital Economy (ICSCDE). :264–267.
In order to improve the big data management ability of IOT access control based on converged network structure, a security integration model of IOT access control based on machine learning and converged network structure is proposed. Combined with the feature analysis method, the storage structure allocation model is established, the feature extraction and fuzzy clustering analysis of big data are realized by using the spatial node rotation control, the fuzzy information fusion parameter analysis model is constructed, the frequency coupling parameter analysis is realized, the virtual inertia parameter analysis model is established, and the integrated processing of big data is realized according to the machine learning analysis results. The test results show that the method has good clustering effect, reduces the storage overhead, and improves the reliability management ability of big data.
2021-03-29
Ateş, Ç, Özdel, S., Anarim, E..  2020.  DDoS Detection Algorithm Based on Fuzzy Logic. 2020 28th Signal Processing and Communications Applications Conference (SIU). :1—4.

While internet technologies are developing day by day, threats against them are increasing at the same speed. One of the most serious and common types of attacks is Distributed Denial of Service (DDoS) attacks. The DDoS intrusion detection approach proposed in this study is based on fuzzy logic and entropy. The network is modeled as a graph and graphics-based features are used to distinguish attack traffic from non-attack traffic. Fuzzy clustering is applied based on these properties to indicate the tendency of IP addresses or port numbers to be in the same cluster. Based on this uncertainty, attack and non-attack traffic were modeled. The detection stage uses the fuzzy relevance function. This algorithm was tested on real data collected from Boğaziçi University network.

2017-02-27
Zhang, L., Li, B., Zhang, L., Li, D..  2015.  Fuzzy clustering of incomplete data based on missing attribute interval size. 2015 IEEE 9th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :101–104.

Fuzzy c-means algorithm is used to identity clusters of similar objects within a data set, while it is not directly applied to incomplete data. In this paper, we proposed a novel fuzzy c-means algorithm based on missing attribute interval size for the clustering of incomplete data. In the new algorithm, incomplete data set was transformed to interval data set according to the nearest neighbor rule. The missing attribute value was replaced by the corresponding interval median and the interval size was set as the additional property for the incomplete data to control the effect of interval size in clustering. Experiments on standard UCI data set show that our approach outperforms other clustering methods for incomplete data.

2015-04-30
Saoud, Z., Faci, N., Maamar, Z., Benslimane, D..  2014.  A Fuzzy Clustering-Based Credibility Model for Trust Assessment in a Service-Oriented Architecture. WETICE Conference (WETICE), 2014 IEEE 23rd International. :56-61.

This paper presents a credibility model to assess trust of Web services. The model relies on consumers' ratings whose accuracy can be questioned due to different biases. A category of consumers known as strict are usually excluded from the process of reaching a majority consensus. We demonstrated that this exclusion should not be. The proposed model reduces the gap between these consumers' ratings and the current majority rating. Fuzzy clustering is used to compute consumers' credibility. To validate this model a set of experiments are carried out.