Thesis Details
Behaviour-Based Identification of Network Devices
This thesis deals with the topic of identifying devices based on their behaviour. With the increasing number of devices on the network, it is becoming more and more important to be able to identify these devices based on their behaviour, due to the increased security risks. General networking concepts and multiple methods that have been used in the past to identify devices are discussed throughout the work. Subsequently, machine learning algorithms and their advantages and disadvantages are introduced. Finally, this thesis tests two traditional machine learning algorithms and proposes two new approaches to network device identification. The resulting final algorithm achieves the accuracy of 89% on a real life data-set with over 10,000 devices using a set of only eight features.
Machine learning, behaviour based identification, network device behaviour, user profiles, classification, decision tree, Naive Bayes Classifier, computer networks, security, device tracking, device identification, text similarity, outlier detection
Grégr Matěj, Ing., Ph.D. (DIFS FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Kořenek Jan, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Malinka Kamil, Mgr., Ph.D. (DITS FIT BUT), člen
Polčák Libor, Ing., Ph.D. (DIFS FIT BUT), člen
@mastersthesis{FITMT22644,
author = "Adam Michael Pol\'{a}k",
type = "Master's thesis",
title = "Behaviour-Based Identification of Network Devices",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2020,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/thesis/22644/"
}