Archivio Istituzionale della RicercaThe 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 / Siracusano, Giuseppe; Trevisan, Martino; Gonzalez, Roberto; Bifulco, Roberto. - ELETTRONICO. - (2019), pp. 2641-2643. (Intervento presentato al convegno 2019 ACM SIGSAC Conference on Computer and Communications Security tenutosi a London, UK nel 11-15 November 2019) [10.1145/3319535.3363276].
On the Application of NLP to Discover Relationships between Malicious Network Entities
Trevisan, Martino;
2019
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.
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https://hdl.handle.net/11583/2766252
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