Integrated Cyber Forensic for E-Mail Analysis Framework
Sally Dakheel Hamdi, Abdulkareem Merhej Radhi · Indian Journal of Science and Technology · 2019
Objectives: This study presents a distinct technique for classifying emails based on data processing and mining, trimming, refinement, and then adapts several algorithms to classify these emails. Methods/Statistical Analysis: SWARM algorithm to obtain practical and accurate results. Findings: The proposed system is capable of learning in an environment with large and variable data. To test the proposed system, we have to select available data which Enron Data set. A high accuracy rate (95%) was obtained, which is higher than the classification rates mentioned in previous research papers presented in section 2 in this paper. Application/ Improvements: In the past two decades, the Internet has become as an open, publicly and widely used as a source of data transmission and exchanging the messages between criminals, terrorists and those who have illegal motivations. Moreover exchanging important data between various military and financial institutions even ordinary citizens. From this view, there is one of the important means of exchanging information widely used on the Internet medium is e-mail. Email messages are digital evidence which have been became one of the important means to adopt by courts in many countries and societies as evidence relied upon in condemnation. Keywords: Digital Forensic, K-means, Learning, Mining, SWARM Algorithm