International Journal Of Engineering Technology And Management Sciences

AUTHOR(S)

Koushik Paul, Sayandeep Paik, Siddhartha Kuri, Soumyadip Majumder, Dr. Avijit Kumar Chaudhuri

DOI: https://doi.org/10.46647/ijetms.2023.v07i02.010

ABSTRACT
The internet is no doubt the biggest and the most important tool of modern civilisation. But along with its numerous benefits, it also comes with its own set of risks, the most important of them being breaches in security and privacy. An anomaly-based Intrusion Detection System (IDS) is a type of security system that is used to detect and alert on unusual or abnormal behaviour that may indicate an attack or intrusion. Unlike signature-based IDS, which rely on known patterns of attack, anomaly-based IDS is designed to detect previously unseen or unknown attacks by identifying deviations from normal patterns of behaviour. Multiple linear regression is a statistical technique used to analyse the relationship between a dependent variable and multiple independent variables. In this technique, a linear equation is established between the dependent variable and multiple independent variables, with the aim of predicting the value of the dependent attribute for a given set of values of the independent attribute. In this paper, we collected a data set of 125974 entries and 42 attributes from Kaggle, pre-processed the data and used logistic regression to predict the dependent variable (called xAttack) using 25 independent variables, as we found a high correlation between the aforementioned variables The results are simulated using 10-fold cross validation, using various train test splits of the data set. The data has been split into 80-20,50-50, and 66-34. After testing the given data set in different train test splits, an accuracy of 92.73 was achieved.

Page No: 75 - 86

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How to Cite This Article:
Koushik Paul, Sayandeep Paik, Siddhartha Kuri, Soumyadip Majumder, Dr. Avijit Kumar Chaudhuri . A Novel Intrusion Detection System Using Multiple Linear Regression . ijetms;7(2):75-86. DOI: 10.46647/ijetms.2023.v07i02.010