• Sally Dakheel Hamdi College of Information Engineering, Al-Nahrain University
  • Abdulkareem M. Radhi
Keywords: Digital Forensic, Mining, SentiWordNet 3.0, Clustering, SWARM, Classification.


The Internet has become open, public and widely used as a source of data transmission and exchanging messages between criminals, terrorists and those who have illegal motivations.  Moreover, it can be used for exchanging important data between various military and financial institutions, or even ordinary citizens. One of the important means of exchanging information widely used on the Internet medium is the e-mail. Email messages are digital evidence that has been become one of the important means to adopt by courts in many countries and societies as evidence relied upon in condemnation, that prompts the researchers to work continuously to develop email analysis tool using the latest technologies to find digital evidence from email messages to assist the forensic expertise into to analyze email groups .This work presents a distinct technique for analyzing and classifying emails based on data processing and extraction, trimming, and refinement, clustering, then using the SWARM algorithm to improve the performance and then adapting support vector machine algorithm to classify these emails to obtain practical and accurate results. This framework, also proposes a hybrid English lexical Dictionary (SentiWordNet 3.0) for email forensic analysis, it contains all the sentiwords such as positive and negative and can deal with the Machine Learning algorithm. The proposed system is capable of learning in an environment with large and variable data. To test the proposed system will be select available data which is Enron Data set. A high accuracy rate is 92% was obtained in best case. The experiment is conducted the Enron email dataset corpus (May 7, 2015 Version of the dataset).

How to Cite
Hamdi, S. D., & Radhi, A. M. (2020). DIGITAL CYBER FORENSIC EMAIL ANALYSIS AND DETECTION BASED ON INTELLIGENT TECHNIQUESINVESTIGATION. Iraqi Journal of Information & Communications Technology, 3(1), 11-25.