NORMA eResearch @NCI Library

Analysis and Detection of Unauthorized Access Points Using various Machine Learning Algorithms

Juwale, Akshay Mangesh (2020) Analysis and Detection of Unauthorized Access Points Using various Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (997kB) | Preview
[img]
Preview
PDF (Configuration manual)
Download (1MB) | Preview

Abstract

Illegal access points in an internet network is a frequent concern for users connected to the network, in this paper, we try to identify the illegal access point entries via RTT Round Trip Time data gathered through all the nodes connected to the network. We will apply machine learning models, in order to correctly classify the access points as authenticated or unauthenticated. Our main objective, in this paper is to study the effectiveness of identification of various illegal access points through various machine learning algorithms such as SVM Support vector machines, KNN K means the nearest neighbour and Genetic algorithms. On performing a comparative analysis among multiple algorithms, we have found that Ant colony Optimization algorithm achieves the highest accuracy of 98.15%. In the proposed work, the synthetic RTT dataset has been generated using network simulation for performing predictive analysis.
Keywords – Illegal Access point, Machine learning, genetic algorithm, network protection.

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: 27 Jan 2021 16:15
Last Modified: 27 Jan 2021 16:15
URI: http://norma.ncirl.ie/id/eprint/4498

Actions (login required)

View Item View Item