VAMO: towards a fully automated malware clustering validity analysis

By Roberto Perdisci, ManChon U. 2012

68 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. George D. Webster
2. Juan Caballero
3. Richard Rivera
4. Marcos Sebastián
5. Apostolis Zarras
6. Bojan Kolosnjaji
7. Platon Kotzias
8. Claudia Eckert

In Computer Networks and Communications

1. George D. Webster
2. Juan Caballero
3. Richard Rivera
4. Marcos Sebastián
5. Apostolis Zarras
6. Bojan Kolosnjaji
7. Platon Kotzias
8. Claudia Eckert

In Signal Processing

1. George D. Webster
2. Juan Caballero
3. Richard Rivera
4. Marcos Sebastián
5. Apostolis Zarras
6. Bojan Kolosnjaji
7. Platon Kotzias
8. Claudia Eckert

Citations in the corpus

• Deep Learning for Classification of Malware System Call Sequences (2016, methodology): “We attempt to extract maximum amount of information from the VirusTotal signatures by performing a simplified version of signature clustering method introduced in VAMO [20].”
• AVclass: A Tool for Massive Malware Labeling (2016, methodology): “Similar to prior work [3, 15, 21, 25, 29] we use the precision, recall, and F1 measure metrics, which we define next.”