Detection of Visual Similarity Snooping Attacks in Emails using an Extended Client Based Technique
George Mwangi Muhindi1, Geoffrey Mariga Wambugu2, Aaron Mogeni Oirere3

1George Mwangi Muhindi*, Master’s Student, Murang’a University of Technology, Kenya.
2Geoffrey Mariga Wambugu, CoD of Information Technology (IT) Department at Murang’a University of Technology, Kenya.
3Aaron Mogeni Oirere, Lecturer and CoD of Computer Science Department at Murang’a University, Kenya.

Manuscript received on March 02, 2021. | Revised Manuscript received on March 16, 2021. | Manuscript published on April 30, 2021. | PP: 24-36 | Volume-10 Issue-4, April 2021. | Retrieval Number: 100.1/ijeat.D22960410421 | DOI: 10.35940/ijeat.D2296.0410421
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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. 
Keywords: Client-based, Naïve Bayes, Snooping Attacks, Technique, Visual Similarity, Security, Emails, Technique, Bayers Theorem.
Scope of the Article: Visual Analytics