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  5. Simulating SQL-Injection Cyber-attacks using GNS3
 
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Simulating SQL-Injection Cyber-attacks using GNS3

Author(s)
Al-Mahrouqi, Aadil  
Tobin, Patrick  
Abdalla, Sameh  
Kechadi, Tahar  
Uri
http://hdl.handle.net/10197/6515
Date Issued
2015-02-13
Date Available
2015-04-29T12:11:28Z
Abstract
Network Forensics is a subtopic of Digital Forensics wherein research on artificat investigations and intrusions evidence acquisition is addressed. Among many challenges in the field, the problem of losing data artifacts in the state of flux, (i.e., live volatile data), when network devices are suddenly non-operational remains a topic of interest to many investigators. The main objective of this article is to simulate an SQL injection attack scenarios in a complex network environment. We designed and simulated a typical Demilitarized Zone (DMZ) network environment using Graphical Network Simulator (GNS3), Virtual Box and VMware workstation. Using this set-up we are now able to simulate specific network devices configuration, perform SQL injection attacks against victim machines and collect network logs. The main motivation of our work is to finally define an attack pathway prediction methodology that makes it possible to examine the network artifacts collected in case network attacks.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
International Journal of Computer Theory and Engineering
Subjects

Machine learning

Statistics

Acquisition

Anti-forensics

Network forensics

SQL injection attack

DOI
10.7763/IJCTE.2016.V8.1046
Language
English
Status of Item
Peer reviewed
Conference Details
The 6th International Conference on Computer Modeling and Simulation (ICCMS 2015), Amsterdam Netherlands, 12-13 February 2015
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

Size

684.25 KB

Format

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Checksum (MD5)

e92d6258f6a15f895dd4ba0b5faf2362

Owning collection
Insight Research Collection
Mapped collections
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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