Intrusion Detection Prediction Using Data Science Technique

Abstract

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. The study has reviewed the state-of-the-art cyber-attack prediction based on Intrusion Alert, its models, and limitations. The ever-increasing frequency and intensity of intrusion attacks on computer networks worldwide intense research efforts towards the design of attack detection and prediction mechanisms. 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. A comparative study between machine learning algorithms had been carried out in order to determine which algorithm is the most accurate in predicting the type cyber attacks. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy, precision, Recall, F1 Score,Sensitivity, and Specificity.