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Published April 30, 2021 | Version v1
Journal article Open

Detection of Visual Similarity Snooping Attacks in Emails using an Extended Client Based Technique

  • 1. Master's Student, Murang'a University of Technology, Kenya.
  • 2. CoD of Information Technology (IT) Department at Murang'a University of Technology, Kenya.
  • 3. Lecturer and CoD of Computer Science Department at Murang'a University, Kenya.
  • 1. Publisher

Description

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.

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Is cited by
Journal article: 2249-8958 (ISSN)

Subjects

ISSN
2249-8958
Retrieval Number
100.1/ijeat.D22960410421