| dc.contributor.author | Muhindi, George M. | |
| dc.date.accessioned | 2021-05-31T12:28:14Z | |
| dc.date.available | 2021-05-31T12:28:14Z | |
| dc.date.issued | 2021-04 | |
| dc.identifier.citation | International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, Volume-10 Issue-4, April 2021 | en_US |
| dc.identifier.issn | 2249-8958 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/4692 | |
| dc.description.abstract | This paper provides an Extended Client Based Technique (ECBT) that performs classification on emails using the Bayessian classifier that attain in-depth defense by performing textual analysis on email messages and attachment extensions to detect and flag snooping emails. The technique was implemented using python 3.6 in a jupyter notebook. An experimental research method on a personal computer was used to validate the developed technique using different metrics. The validation results produced a high acceptable percentage rate based on the four calculated validation metrics indicating that the technique was valid. The cosine of similarity showed a high percentage rate of similarity between the validation labels indicating that there is a high rate of similarity between the known and output message labels. The direction for further study on this paper is to conduct a replica experiments, which enhances the classification and flagging of the snooped emails using an advanced classification method. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Client-based, Naïve Bayes, Snooping Attacks, Technique, Visual Similarity, Security, Emails, Technique, Bayers Theorem. | en_US |
| dc.title | Detection of Visual Similarity Snooping Attacks in Emails using an Extended Client Based Technique | en_US |
| dc.type | Article | en_US |
