Name
Title | ||
|---|---|---|
Novel set of general descriptive features for enhanced detection of malicious emails using machine learning methods. |
Abstract | ||
|---|---|---|
•We propose a novel set of general descriptive features for malicious email detection.•We leverage our features with ML for the detection of malicious email.•Our novel set of features enhances the detection of malicious email using ML.•The classifier which provided the best detection capabilities was Random Forest.•The best detection results were AUC = 0.929, TPR = 0.947, and FPR = 0.03. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.eswa.2018.05.031 | Expert Systems with Applications |
Keywords | Field | DocType |
Email,Detection,Machine learning,Analysis,Malware,Features | False positive rate,Confidentiality,Computer science,Business operations,Artificial intelligence,Header,Internet access,Random forest,True positive rate,Machine learning,Learning classifier system | Journal |
Volume | ISSN | Citations |
110 | 0957-4174 | 0 |
PageRank | References | Authors |
0.34 | 35 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aviad Cohen | 1 | 58 | 7.35 |
| Nir Nissim | 2 | 199 | 19.42 |
| Yuval Elovici | 3 | 2583 | 204.53 |