Network visualisation and analysis
The Border Gateway Protocol (BGP) plays a vital role in how different networks, knownasAutonomoussystems (AS), communicate across the internet. However, BGP is not perfect and is vulnerable to several types of anomalies like route hijacking, route leakage, and prefix hijacking that can potenti...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180849 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-180849 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1808492024-10-30T00:58:34Z Network visualisation and analysis Neo, Wei Lee Bu Sung, Francis College of Computing and Data Science EBSLEE@ntu.edu.sg Computer and Information Science BGP The Border Gateway Protocol (BGP) plays a vital role in how different networks, knownasAutonomoussystems (AS), communicate across the internet. However, BGP is not perfect and is vulnerable to several types of anomalies like route hijacking, route leakage, and prefix hijacking that can potentially affect both the stability and security of the internet. In this project, the use of Geometric Deep Learning, specifically Graph Neural Net works (GNNs), to detect anomalies in BGP routing data. GNNs, particularly Graph Convolution Networks (GCNs) and GraphAttention Networks (GANs), are well-suited for this task because they can learn from graph-structured data. BGP routing informa tion was represented as a graph-like structure, where the nodes are autonomous systems and the edges are the BGP updates between them to enable the GNN models to capture the complex relationships and patterns in the network. For data extraction BGPStream and BGP Machine Learning (BML) framework was used to extract historical data from many different sources and utilized to process the data in converting it into a graph format that could be fed into the GNN models for training. In summary, the results from the project showed that GNNs particularly GANs out performed GCN in the task of BGP route anomaly detection. The ability of GAN coupled with the attention mechanism to understand the graph-like structure of BGP routing data allowed the accurate identification of unusual events with a higher level of precision compared to the GCN have a strong potential for improving the detection tasks of network anomalies and by taking advantage of the natural structure of BGP routing data, these models could help enhance the security and stability of the internet. Bachelor's degree 2024-10-30T00:58:34Z 2024-10-30T00:58:34Z 2024 Final Year Project (FYP) Neo, W. (2024). Network visualisation and analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/180849 https://hdl.handle.net/10356/180849 en SCSE23-0896 application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Computer and Information Science BGP |
spellingShingle |
Computer and Information Science BGP Neo, Wei Network visualisation and analysis |
description |
The Border Gateway Protocol (BGP) plays a vital role in how different networks,
knownasAutonomoussystems (AS), communicate across the internet. However, BGP
is not perfect and is vulnerable to several types of anomalies like route hijacking, route
leakage, and prefix hijacking that can potentially affect both the stability and security
of the internet.
In this project, the use of Geometric Deep Learning, specifically Graph Neural Net
works (GNNs), to detect anomalies in BGP routing data. GNNs, particularly Graph
Convolution Networks (GCNs) and GraphAttention Networks (GANs), are well-suited
for this task because they can learn from graph-structured data. BGP routing informa
tion was represented as a graph-like structure, where the nodes are autonomous systems
and the edges are the BGP updates between them to enable the GNN models to capture
the complex relationships and patterns in the network.
For data extraction BGPStream and BGP Machine Learning (BML) framework was
used to extract historical data from many different sources and utilized to process the
data in converting it into a graph format that could be fed into the GNN models for
training.
In summary, the results from the project showed that GNNs particularly GANs out
performed GCN in the task of BGP route anomaly detection. The ability of GAN
coupled with the attention mechanism to understand the graph-like structure of BGP
routing data allowed the accurate identification of unusual events with a higher level
of precision compared to the GCN have a strong potential for improving the detection
tasks of network anomalies and by taking advantage of the natural structure of BGP
routing data, these models could help enhance the security and stability of the internet. |
author2 |
Lee Bu Sung, Francis |
author_facet |
Lee Bu Sung, Francis Neo, Wei |
format |
Final Year Project |
author |
Neo, Wei |
author_sort |
Neo, Wei |
title |
Network visualisation and analysis |
title_short |
Network visualisation and analysis |
title_full |
Network visualisation and analysis |
title_fullStr |
Network visualisation and analysis |
title_full_unstemmed |
Network visualisation and analysis |
title_sort |
network visualisation and analysis |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/180849 |
_version_ |
1814777766147522560 |