Name
Abstract | ||
|---|---|---|
We present an investigation analysis approach for mining anonymous email content. The core idea behind our approach is concentrated on collecting various effective features from previous emails for all the possible suspects. The extracted features are then used with several machine learning algorithms to extract a unique writing style for each suspect. A sophisticated comparison between the investigated anonymous email and the suspects writing styles is employed to extract evidence of the possible email sender. Extensive experimental results on a real data sets show the improved performance of the proposed method with very limited number of features. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1504/IJSN.2013.055941 | IJSN |
Keywords | Field | DocType |
extensive experimental result,improved performance,core idea,possible suspect,anonymous email content,investigation analysis approach,email authorship identification,possible email sender,anonymous email,limited number,unique writing style,simplified feature,digital forensics,writing styles,machine learning,data mining | Data mining,HTML email,Email address harvesting,Information retrieval,Digital forensics,Computer science,Computer security,Writing style,Communication source,Suspect,Cyber crime | Journal |
Volume | Issue | Citations |
8 | 2 | 3 |
PageRank | References | Authors |
0.45 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emad E. Abdallah | 1 | 112 | 12.57 |
| Alaa E. Abdallah | 2 | 20 | 3.29 |
| Mohammad Bsoul | 3 | 90 | 12.41 |
| Ahmed F. Otoom | 4 | 3 | 0.79 |
| Essam Al-daoud | 5 | 18 | 4.77 |