A Comparative Study on Detection of Malware and Benign on the Internet Using Machine Learning Classifiers

Abstract

The exponential growth in network usage has opened the way for people who use the Internet to be exploited. A phishing attack is the most effective way to obtain sensitive information about a target individual without their knowledge over the Internet. Phishing detection has an increased false-positive rate and is inaccurate. The motivation behind the research is to analyze and classify the applications among malware or benign with less time complexity. The main purpose is to find the algorithm which provides better accuracy for detecting the adware. The comparative analysis was made with three machine learning classifiers to find a better one. Random forest, SVM, and naïve Bayes were selected because of the better results obtained in previous research papers. Using a confusion matrix, the classifier methods were evaluated for accuracy, precision, recall, and F-measure with positive rates of both true and false. This research indicates that there are a number of classifiers that, if accurately detected, offer better reliable phishing detection outcomes. Random forest has proven to be an effective classifier with 0.9947 accuracy and a 0.017 false-positive rate. In this study, the comparative analysis reveals that the best ML classifiers have a lesser prediction accuracy for spoofing threat identification, implying that nonphishing programmers can use the best ML classifiers to evaluate the attributes of spoofing threat recognition and classification.