Titlebook: Data Analytics and Decision Support for Cybersecurity; Trends, Methodologie Iván Palomares Carrascosa,Harsha Kumara Kalutarage Book 2017 Sp

Tartar

Attribute Noise, Classification Technique, and Classification Accuracyification depends on various aspects. Though the data classification technique has a major impact on classification accuracy, the nature of the data also matters lot. One of the main concerns that can hinder the classification accuracy is the availability of noise. Therefore, both choosing the appro

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Visualization and Data Provenance Trends in Decision Support for Cybersecurityfor cybersecurity. Current analytical time taken to identify zero-day attacks and respond with a patch or detection mechanism is unmeasurable. This is a current challenge and struggle for cybersecurity researchers. User- and data provenance-centric approaches are the growing trend in aiding defensiv

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https://doi.org/10.1057/9780230623781ithin a dynamic and ever-changing organisation? More recently, there has been interest in how visual analytics can be incorporated with machine-based approaches, to alleviate the data analytics challenges of anomaly detection and to support human reasoning through visual interactive interfaces. Furt

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https://doi.org/10.1007/978-3-030-14869-0ese data to improve the performance of the decision engine. Finally, the decision engine is designed based on the Dirichlet mixture model with a lower-upper interquartile range as decision engine. The performance of this framework is evaluated on two well-known datasets, the NSL-KDD and UNSW-NB15. T

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欢呼

Reem Wagdy Moustafa Kamel Abd El-Raoof data about online credit-card transactions. According to the empirical outcomes, we find that the noise hinders the classification accuracy significantly. In addition, the results indicate that the accuracy of data classification depends on the quality of the data and the used classification techni

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https://doi.org/10.1007/978-981-19-1376-1he overall goal of the intelligent system is to provide a degree of possibility as to whether the load demand is legitimate or it has been manipulated in a way that is a threat to the safety of the node and that of the grid in general. The proposed system is tested with real-world data.

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