Emails are frequently utilized as a way of personal and professional communication. Banking information, credit reports, login data, and other sensitive and personal information are frequently transmitted over email. ...
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ISBN:
(纸本)9781665483032
Emails are frequently utilized as a way of personal and professional communication. Banking information, credit reports, login data, and other sensitive and personal information are frequently transmitted over email. This makes them valuable to cyber criminals, who can exploit the knowledge for their own gain. Phishing is a technique used by con artists to steal sensitive information from people by impersonating well-known sources. The sender of a phished email can persuade you to disclose personal information under false pretenses. The detection of a phished email is treated as a classification problem in this research, and this paper shows how machine learning methods are used to categorize emails as phished or not. LMT classifiers attain a maximum accuracy in email classification.
As a classical clustering algorithm, K-means algorithm has a profound research background. In the of big data era, K-means algorithms will play a greater advantage, being able to quickly divide similar data into the s...
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As a classical clustering algorithm, K-means algorithm has a profound research background. In the of big data era, K-means algorithms will play a greater advantage, being able to quickly divide similar data into the same cluster. Combining K-means algorithm with MapReduce distributed computing framework and running on Hadoop big data platform can significantly improve the clustering effect. Based on MapReduce framework structure, this paper studies K-means model, including K-means principle, distance calculation, content validity index and external validity index. On this basis, the K-means clustering flow based on MapReduce big data programming framework is proposed, and the execution process of the algorithm flow is described in detail, which provides a guide for the algorithm implementation.
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