The increase in network traffic volumes challenges the scalability of security analysis tools. In this paper, we present NetLearn, a solution to identify potentially malicious network entities from large amounts of network traffic data. NetLearn applies recently developed natural language processing algorithms to discover securityrelevant relationships between the observed network entities, e.g., domain names and IP addresses, without requiring external sources of information for its analysis.
On the Application of NLP to Discover Relationships between Malicious Network Entities
Trevisan, Martino;
2019-01-01
Abstract
The increase in network traffic volumes challenges the scalability of security analysis tools. In this paper, we present NetLearn, a solution to identify potentially malicious network entities from large amounts of network traffic data. NetLearn applies recently developed natural language processing algorithms to discover securityrelevant relationships between the observed network entities, e.g., domain names and IP addresses, without requiring external sources of information for its analysis.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
p2641-siracusano.pdf
Accesso chiuso
Licenza:
Copyright dell'editore
Dimensione
888.13 kB
Formato
Adobe PDF
|
888.13 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
