A Phishing detection model based on dynamic-hybrid feature selection

Simple item page
dc.contributor.authorRuiru, Daniel K
dc.date.accessioned2021-07-28T12:56:40Z
dc.date.available2021-07-28T12:56:40Z
dc.date.issued2019-07
dc.descriptionThesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Information Technologyen_US
dc.description.abstractPhishing attacks have been a big internet nuisance since the early 1990s when hackers started stealing information from organizations using messaging platforms. At the time, the problem affected large institutions and corporations as the internet was still in its early stages of development and, had minimal individual subscribers. The early 2000s saw the widespread application of the technology (internet) which subsequently saw phishers target individual users using electronic mails (emails). In itself, phishing is a form of cyber-attack that steals personal information from unsuspecting users by duping them using verification or reward emails. This deception process ultimately helps the intruders to access sensitive data that can be used to access financial records for monetary gains or identity theft. Phishing attacks are so prevalent today that over 95 percent of all cyber-attacks are characterized by their intrusion procedures. Moreover, the attacks seem to increase each year and based on recent surveys are said to have a 60 percent annual growth rate. It is because of this outcome that this research proposes a predictive model to detect phishing attacks by implementing a system that pre-empts the intrusion processes before they happen. Unlike conventional methods that rely on human expertise to mitigate the problem, the proposed model automates the identification of the attacks and subsequently their control. This research aims to achieve this goal by optimizing the selection of subset features using a dynamic model that analyses the structural properties of phishing attacks to get adaptive attributes (features) for detecting phishing threats (as highlighted in chapter 4). Random forest is then used as the final classifier owing to its accuracy results (84.13%). Ultimately, the study then proposes the construction of a base model for bootstrapping other detection models in the cyber-security world.en_US
dc.description.sponsorshipFaculty of Information Technologyen_US
dc.identifier.urihttp://hdl.handle.net/11071/12052
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectPhishersen_US
dc.subjectPhishing attacksen_US
dc.subjectNeural Networksen_US
dc.subjectUniform Resource Locatoren_US
dc.titleA Phishing detection model based on dynamic-hybrid feature selectionen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A Phishing detection model based on dynamic-hybrid feature selection.pdf
Size:
1.4 MB
Format:
Adobe Portable Document Format
Description:
Full-text thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
MSIT Theses and Dissertations (2019)