A MACHINE LEARNING BASED LANGUAGE MODEL TO IDENTIFY COMPROMISED USERS
| dc.contributor.author | Phan, Tien Jr | |
| dc.contributor.copyright-release | No | en_US |
| dc.contributor.degree | Master of Computer Science | en_US |
| dc.contributor.department | Faculty of Computer Science | en_US |
| dc.contributor.ethics-approval | Not Applicable | en_US |
| dc.contributor.external-examiner | n/a | en_US |
| dc.contributor.graduate-coordinator | Dr. Norbert Zeh | en_US |
| dc.contributor.manuscripts | No | en_US |
| dc.contributor.thesis-reader | Dr. Andrew McIntyre | en_US |
| dc.contributor.thesis-reader | Dr. Malcolm Heywood | en_US |
| dc.contributor.thesis-supervisor | Dr. Nur Zincir-Heywood | en_US |
| dc.date.accessioned | 2018-04-19T10:35:41Z | |
| dc.date.available | 2018-04-19T10:35:41Z | |
| dc.date.defence | 2018-04-17 | |
| dc.date.issued | 2018-04-19T10:35:41Z | |
| dc.description.abstract | Identifying compromised accounts on online social networks that are used for phishing attacks or sending spam messages is still one of the most challenging problems of cyber security. In this research, the author explore an artificial neural network based language model to differentiate the writing styles of different users on short text messages. In doing so, the aim is to be able to identify compromised user accounts. The results obtained indicate that one can learn the language model on one dataset and can generalize it to different datasets with high accuracy and low false alarm rates without any modifications to the language model. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/73875 | |
| dc.language.iso | en | en_US |
| dc.subject | Compromised users | en_US |
| dc.subject | Forensics analysis | en_US |
| dc.subject | Language model | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.title | A MACHINE LEARNING BASED LANGUAGE MODEL TO IDENTIFY COMPROMISED USERS | en_US |