A COMPARATIVE STUDY AND ANALYSIS OF NAIVE BAYES ALGORITHM, SUPPORT VECTOR MACHINE AND APRIORI DATA MINING ALGORITHMS FOR INTRUSION DETECTION SYSTEMS (IDS)

Abstract

The purpose ofthis project is to compare, contrast and examine naive Bayes, apriori algorithms and support vector machine for IDS. This project focuses on data mining concepts and techniques that give meaning to data and classification techniques which are used in data mining which includes Anomaly detection, Regression, Association rule learning, Clustering, summarization and regression, different mining tools to solve the problems. We use some research methods involving tracking patterns which is used to recognize data patterns in data set. There are We used supervised learning in this project because supervised learning uses training data for inferred function which helps in mapping the new examples. It helps to identify the class labels ofthe instances which are unseen. It generalizes the training data from unseen data according to general ways.