NORMA eResearch @NCI Library

Effectively improving the efficiency and performance of an intrusion detection system using hybrid machine learning models

Alladi, Sumanth (2020) Effectively improving the efficiency and performance of an intrusion detection system using hybrid machine learning models. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (405kB) | Preview
[img]
Preview
PDF (Configuration manual)
Download (913kB) | Preview

Abstract

Due to widespread usage of internet we need a proper security network which plays a crucial role by securing the information, as the usage of internet increasing the attackers are also widely increasing. Though we have lot of security systems, hackers are actively using new techniques to overcome this security. Intrusion Detection System (IDS) is a system that provides a security layer to the organization network and it plays a crucial role by blocking the malicious attacks at the initial point of the organization. Here in this research I proposed an IDS hybrid model with Logistic regression with K-means clustering and MLP (MultiLayer Perceptron) with K-Means clustering. I had chosen NSL-KDD dataset to demonstrate the working of algorithm by testing the dataset and to show the difference between malicious and normal flow of network traffic.
Keywords: Intrusion Detection System, hybrid algorithms, k-means clustering and MLP algorithm.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Dan English
Date Deposited: 26 Jan 2021 13:40
Last Modified: 26 Jan 2021 13:40
URI: http://norma.ncirl.ie/id/eprint/4484

Actions (login required)

View Item View Item