Network Intrusion Prediction using Machine Learning

Abstract

Intrusion Detection Systems are designed to safeguard the security needs of enterprise networks against cyber-attacks. However, networks suffer from several limitations, such as generating a high volume of low-quality alerts. While there are a variety of intrusion detection solutions available, the prediction of network intrusion events is still under active investigation. Over the past, statistical methods have dominated the design of attack prediction methods. The analysis of dataset by Supervised Machine Learning Technique(SMLT) to capture several information’s like, variable identification, univariate analysis, bivariate and multivariate analysis, missing value treatments etc. Then, we going to implement a machine-learning algorithm to get better accuracy. After that, we can build looks like a web page by using Django Framework. Once the user passed the information then the Admin can see the output, and after he will send the response to the user. The result shows the effectiveness of the machine learning metrics which are accuracy, precision, Recall, F1 Score, Sensitivity, and Specificity