New Spam Filtering Method with Hadoop Tuning-Based MapReduce Na?ve Bayes

As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing. Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naive Bayes among machine learning methods for malicious email filtering. Naive Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbor(KNN), and Decision Tree) in terms of execution time and accuracy. Malicious email was filtered with MapReduce programming using the Naive Bayes technique, which is a supervised machine learning method, in a Hadoop framework with optimized performance and also with the Python program tech-nique with the Naive Bayes technique applied in a bare metal server environment with the Hadoop environment not applied. According to the results of a comparison of the accuracy and predictive error rates of the two methods, the Hadoop MapReduce Naive Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naive Bayes method.