Provenance graph generation for intrusion detection

Provenance is defined as the origin or the earliest known history of a thing. In the aspect of data provenance, it defines the origin of a data and how it was created, and actions performed on the data. These data could be used for forensics and security. This project aims to capture whole syst...

Full description

Saved in:
Bibliographic Details
Main Author: Jabir Shah Halith
Other Authors: Ke Yiping, Kelly
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/162932
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-162932
record_format dspace
spelling sg-ntu-dr.10356-1629322022-11-14T05:19:47Z Provenance graph generation for intrusion detection Jabir Shah Halith Ke Yiping, Kelly School of Computer Science and Engineering ypke@ntu.edu.sg Engineering::Computer science and engineering Provenance is defined as the origin or the earliest known history of a thing. In the aspect of data provenance, it defines the origin of a data and how it was created, and actions performed on the data. These data could be used for forensics and security. This project aims to capture whole system provenance to detect any intrusion. There are multiple systems to capture the provenance such as Provenance Aware Storage System(PASS), Hi-Fi, Linux Provenance Module (LPM), CamFlow. This project focuses on setting up CamFlow, a whole-system provenance capture mechanism. The data captured from various intrusion scenarios using the CamFlow system would be streamed to Flurry. Flurry is a web server based; end-to-end data pipeline connected to CamFlow to generate provenance graphs. This project shows how CamFlow, and Flurry can be integrated to analyse for any intrusion in systems. Bachelor of Engineering (Computer Science) 2022-11-14T05:19:46Z 2022-11-14T05:19:46Z 2022 Final Year Project (FYP) Jabir Shah Halith (2022). Provenance graph generation for intrusion detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162932 https://hdl.handle.net/10356/162932 en 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 Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Jabir Shah Halith
Provenance graph generation for intrusion detection
description Provenance is defined as the origin or the earliest known history of a thing. In the aspect of data provenance, it defines the origin of a data and how it was created, and actions performed on the data. These data could be used for forensics and security. This project aims to capture whole system provenance to detect any intrusion. There are multiple systems to capture the provenance such as Provenance Aware Storage System(PASS), Hi-Fi, Linux Provenance Module (LPM), CamFlow. This project focuses on setting up CamFlow, a whole-system provenance capture mechanism. The data captured from various intrusion scenarios using the CamFlow system would be streamed to Flurry. Flurry is a web server based; end-to-end data pipeline connected to CamFlow to generate provenance graphs. This project shows how CamFlow, and Flurry can be integrated to analyse for any intrusion in systems.
author2 Ke Yiping, Kelly
author_facet Ke Yiping, Kelly
Jabir Shah Halith
format Final Year Project
author Jabir Shah Halith
author_sort Jabir Shah Halith
title Provenance graph generation for intrusion detection
title_short Provenance graph generation for intrusion detection
title_full Provenance graph generation for intrusion detection
title_fullStr Provenance graph generation for intrusion detection
title_full_unstemmed Provenance graph generation for intrusion detection
title_sort provenance graph generation for intrusion detection
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/162932
_version_ 1751548489732456448