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...

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Bibliographic Details
Main Author: Neo, Wei
Other Authors: Lee Bu Sung, Francis
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
BGP
Online Access:https://hdl.handle.net/10356/180849
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Institution: Nanyang Technological University
Language: English
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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
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