Analyzing the likeness of a person based on DNS logs using machine learning

Abstract

In a technology filled world with a lot of online data it is very hard to find a person’s attitude or behavior or his/her likeness. This project, which can predict the likeness of the person using their online logs can be used for this. The present study of a person is based on their online activities, but to identify which category they like the most comes from their personal behavior on the domains they visit. This particular project finds the likeness of a person based on their most liked webpages. This uses the records of DNS logs and tries to identify the most seen webpages and figures out which category they like the most. The program used in this project is a multiclass classification model that would classify and predict the type of webpage the user has visited the most. This will help in effectively predicting the particular person’s likeness. With this method tests were conducted with three main algorithms, Support Vector Machine, Convolutional Neural Network and Naive Bayes out of which we were able to get an accuracy of 95% using the Naive Bayes algorithm, which helped in predicting the user’s likeness. This can further be enhanced with much higher real time log activity finder and real time log analyser which helps in finding or keeping a track of the person’s behavior. This program can widely be used to study humans.