- author = "Nicolas Sourbier",
- title = "Learning-based Intrusion Detection: An imbalanced, constantly evolving and Timely Problem",
- school = "Institut National des Sciences Appliquees de Rennes (INSA)",
- year = "2022",
- address = "20, avenue des Buttes de Coesmes - CS 70839, 35708 RENNES Cedex 7, France",
- month = "29 " # sep,
- keywords = "genetic algorithms, genetic programming, Tangled Program Graphs, IETR Vaader",
-
URL = "
https://www.ietr.fr/soutenance-de-these-de-nicolas-sourbier",
-
URL = "https://theses.hal.science/tel-04522156/",
-
URL = "
https://www.insa-rennes.fr/fileadmin/ressources/Actus/Theses/2022/SOURBIER_resume.pdf",
-
video_url = "https://www.youtube.com/watch?v=4hp1TChni9M",
-
abstract = "Network Intrusion Detection Systems (NIDS) observe a
network environment and aim to identify intrusions:
malicious behaviors that compromise integrity,
confidentiality or availability of either the network
data or the systems. NIDS can be classified into
signature-based NIDS, that identify known intrusions by
comparing the traffic with a knowledge base, and
anomaly-based NIDS (AIDS) that aim to qualify the
unknown intrusion traffic from a model of normal
traffic. AIDS are mostly based on Machine Learning
techniques. Performing detection of rare events such as
intrusions in an ever-changing network environment
using learned AIDS is a challenge bound to several big
issues. Firstly, gathering representative network data
with accurate label information is costly. These data
are also highly imbalanced as intrusions are rare
events.
Finally, there is no guarantee that a learned AIDS on a network intrusion detection dataset is useful for real NIDS inference. explores the capabilities of the Tangled Program Graphs (TPG) framework to act as an AIDS probe. TPG is a form of machine learning based on genetic programming that offers lightweight and versatile learning capabilities.",
- notes = "Supervisor: Maxime Pelcat",
