Full metadata
Title
Identifying Financial Frauds on Darkweb
Description
Data breaches have been on a rise and financial sector is among the top targeted. It can take a few months and upto a few years to identify the occurrence of a data breach. A major motivation behind data breaches is financial gain, hence most of the data ends up being on sale on the darkweb websites. It is important to identify sale of such stolen information on a timely and relevant manner. In this research, we present a system for timely identification of sale of stolen data on darkweb websites. We frame identifying sale of stolen data as a multi-label classification problem and leverage several machine learning approaches based on the thread content (textual) and social network analysis of the user communication seen on darkweb websites. The system generates alerts about trends based on popularity amongst the users of such websites. We evaluate our system using the K-fold cross validation as well as manual evaluation of blind (unseen) data. The method of combining social network and textual features outperforms baseline method i.e only using textual features, by 15 to 20 % improved precision. The alerts provide a good insight and we illustrate our findings by cases studies of the results.
Date Created
2018
Contributors
- Dharaiya, Krishna Tushar (Author)
- Shakarian, Paulo (Thesis advisor)
- Doupe, Adam (Committee member)
- Shoshitaishvili, Yan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
46 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.49147
Level of coding
minimal
Note
Masters Thesis Computer Science 2018
System Created
- 2018-06-01 08:02:57
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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