Analysis of supervised and unsupervised technique for authentication dataset

 
 
 
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  • Abstract


    Traditional methods of data storage vary from the present. These days data has become more unstructured and requires to be read contextually. Data Science provides a platform for the community to perform artificial intelligence and deep learning methodologies on large volumes of structured and unstructured data. In the era of artificial intelligence, AI is showing it’s true potential by addressing social causes and automation in various industries such as automobile, medicine and smart buildings, healthcare, retail, banking, and finance service are some of the deliverables. From a variety of sources and flooding data, AI and machine learning are finding real-world adoption and applications. The nature of the data models is trial and error and is prone to change with their discoveries for the specific problem and this is the case with the different algorithms used. In this paper, we apply machine learning algorithms such as unsupervised learning k-means, bat k-means and supervised learning decision tree, k-NN, support vector machine, regression, discriminant analysis, ensemble classification for data set taken from UCI repository, phishing website, website phishing, Z- Alizadeh Sani and authentication datasets. Authentication dataset is generated for testing Single Sign-on which learns from data by training to make predictions.


  • References


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Article ID: 21503
 
DOI: 10.14419/ijet.v7i4.21503




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